# Best Text Analysis Software

  *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*

   Text analysis software, also called text analytics or text mining software, helps users gain insights from both structured and unstructured text data using natural language processing (NLP). Such insights include sentiment analysis, key phrases, language, themes and patterns, and entities, among others. These solutions leverage [NLP](https://www.g2.com/categories/natural-language-processing-nlp) and [machine learning](https://www.g2.com/categories/machine-learning) to pull out different insights and provide visual representations of the data for easier interpretation.

Text analysis tools can consume text data from a variety of sources, including emails, phone transcripts, surveys, customer reviews, and other documents. By importing text data from these different sources, businesses are better equipped to understand and analyze customer or employee sentiment, intelligently classify documents, and improve written content. Text analysis software may be used in conjunction with other analytics tools, including [big data analytics](https://www.g2.com/categories/big-data-analytics) and [business intelligence platforms](https://www.g2.com/categories/business-intelligence-platforms).

To qualify for the Text Analysis category, a product must:

- Import text data from a variety of different data sources
- Use natural language processing to extract insights from the text, including key phrases, language, sentiment, and other patterns
- Provide visualizations for text data





## Category Overview

**Total Products under this Category:** 188


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 5,200+ Authentic Reviews
- 188+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Best Text Analysis Software At A Glance

- **Leader:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Highest Performer:** [Canvs](https://www.g2.com/products/canvs-ai-canvs/reviews)
- **Easiest to Use:** [Kimola](https://www.g2.com/products/kimola/reviews)
- **Top Trending:** [Unwrap.ai](https://www.g2.com/products/unwrap-ai/reviews)
- **Best Free Software:** [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews)


---

**Sponsored**

### Blix

Blix offers an AI-powered text analysis solution designed to retrieve insights from customer feedback quickly and effortlessly. The platform automates the process of coding survey open ends, online reviews, and other textual feedback, turning them into insights without the need for manual effort. Blix’s automated topic discovery scans through open text feedback and identifies key themes and topics. Its AI-powered coding transforms unstructured text into structured, quantitative data, eliminating the need for manual coding. Users receive automated reports and summaries. The platform supports multiple languages and offers data encryption and strict confidentiality guarantees.



[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1260&secure%5Bdisplayable_resource_id%5D=1260&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1260&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1393471&secure%5Bresource_id%5D=1260&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Ftext-analysis%2Fmid-market&secure%5Btoken%5D=a5c301c6d871359de77d6e9913668d035727a6f280ef948d44fe20a5fe1d3141&secure%5Burl%5D=https%3A%2F%2Fblix.ai&secure%5Burl_type%5D=company_website)

---

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
  SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 725

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.5/10 (Category avg: 8.1/10)
- **Compositionality:** 8.1/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 7.6/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** https://www.sas.com/
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,996 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, Statistical Programmer
  - **Top Industries:** Pharmaceuticals, Computer Software
  - **Company Size:** 33% Enterprise, 32% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (316 reviews)
- Features (218 reviews)
- Analytics (196 reviews)
- Data Analysis (166 reviews)
- User Interface (147 reviews)

**Cons:**

- Learning Difficulty (151 reviews)
- Learning Curve (144 reviews)
- Complexity (143 reviews)
- Difficult Learning (117 reviews)
- Expensive (108 reviews)

  ### 2. [Google Cloud Natural Language API](https://www.g2.com/products/google-cloud-natural-language-api/reviews)
  Derive insights from unstructured text using Google machine learning.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.7/10 (Category avg: 8.1/10)
- **Compositionality:** 8.8/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.3/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 55% Small-Business, 24% Enterprise


#### Pros & Cons

**Pros:**

- Application Development (1 reviews)
- Cloud Computing (1 reviews)
- Features (1 reviews)

**Cons:**

- Not User-Friendly (1 reviews)

  ### 3. [Chattermill](https://www.g2.com/products/chattermill/reviews)
  Chattermill is the customer experience intelligence and Voice of Customer (VoC) platform designed to help organizations effectively unify and analyze their customer feedback across any channel. By leveraging advanced AI technology, Chattermill empowers businesses to extract meaningful insights from diverse data sources, including surveys, reviews, support tickets, conversations, and social media interactions. This comprehensive approach enables companies to identify recurring issues, understand customer pain points, and drive product improvements with confidence. Targeted primarily at customer-focused teams, Chattermill serves a wide range of industries, including e-commerce, hospitality, and retail. Organizations like Uber, HelloFresh, Booking.com, Tesco, JustEat, and H&M utilize Chattermill to transform their customer experiences and foster business growth. The platform is particularly beneficial for Customer Experience (CX) and Voice of Customer (VoC) teams, as it allows them to pinpoint factors affecting customer satisfaction and loyalty. Additionally, Product and UX teams can prioritize enhancements based on genuine customer needs, while Support and Operations teams can identify recurring issues before they escalate into larger problems. One of Chattermill's key features is its powerful AI analytics capability, which enables the extraction of actionable insights from unstructured text feedback. This functionality allows businesses to uncover clear trends and patterns that inform strategic decision-making. By providing clarity and insights, Chattermill helps organizations improve their products and services, ultimately leading to increased customer satisfaction. The platform's ability to consolidate and scale voice-of-customer analysis also benefits Insights and Data teams, making it easier to manage and interpret large volumes of feedback. Chattermill stands out in the feedback analytics category due to its commitment to delivering deep, actionable insights rather than just surface-level metrics. This focus on understanding customer sentiment allows organizations to make informed decisions that enhance their overall customer experience. The platform has received recognition from G2, being named a Grid Leader and Momentum Leader in Feedback Analytics Products, among other accolades. These distinctions highlight Chattermill's effectiveness and user satisfaction within the competitive landscape of customer feedback solutions. For organizations looking to deepen their understanding of customer experiences and drive meaningful improvements, Chattermill offers a robust solution that integrates seamlessly into existing workflows. By harnessing the power of AI and comprehensive feedback analysis, businesses can navigate the complexities of customer sentiment and foster lasting loyalty.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 216

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.9/10 (Category avg: 8.1/10)
- **Compositionality:** 7.7/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.0/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Chattermill](https://www.g2.com/sellers/chattermill)
- **Company Website:** https://chattermill.com/
- **Year Founded:** 2015
- **HQ Location:** London
- **Twitter:** @ChattermillAI (459 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/9443815/ (74 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Senior Product Manager, Product Manager
  - **Top Industries:** Retail, Financial Services
  - **Company Size:** 50% Mid-Market, 42% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (48 reviews)
- Feedback Management (38 reviews)
- Customer Insights (35 reviews)
- Insights Generation (35 reviews)
- Insights Analysis (30 reviews)

**Cons:**

- Not Intuitive (12 reviews)
- Complex Usability (10 reviews)
- Inaccuracy (10 reviews)
- Insufficient Information (10 reviews)
- AI Limitations (9 reviews)

  ### 4. [Canvs](https://www.g2.com/products/canvs-ai-canvs/reviews)
  Businesses struggle to understand the true meaning behind customer feedback, and how to action it. Canvs solves this by using advanced AI to analyze unstructured data, turning complex customer sentiments into clear, actionable intelligence. By revealing the emotional drivers behind customer behavior, Canvs enables brands to make more empathetic, data-driven decisions that boost customer loyalty, drive innovation, and create deeper connections with their audience.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 147

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.2/10 (Category avg: 8.1/10)
- **Compositionality:** 7.6/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 7.4/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Canvs AI](https://www.g2.com/sellers/canvs-ai)
- **Company Website:** https://canvs.ai
- **Year Founded:** 2010
- **HQ Location:** New York, New York
- **Twitter:** @canvsai (2,671 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/canvsai/ (30 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Market Research, Entertainment
  - **Company Size:** 41% Enterprise, 32% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (21 reviews)
- AI Technology (14 reviews)
- Insights Generation (14 reviews)
- Customer Support (12 reviews)
- Helpful (12 reviews)

**Cons:**

- AI Limitations (5 reviews)
- Inaccuracy (5 reviews)
- Slow Performance (5 reviews)
- Software Instability (5 reviews)
- Accuracy Issues (4 reviews)

  ### 5. [Caplena](https://www.g2.com/products/caplena/reviews)
  Caplena is the feedback intelligence layer that helps brands and research teams turn open-ended feedback into precise, actionable insights — without the rigidity of traditional CX platforms. Built with Swiss precision, Caplena combines deep analytical power, unmatched flexibility, and intuitive simplicity. Teams can analyze any feedback source with human level accuracy, refine themes interactively, and model datasets independently — no data scientists required. Trusted by 200+ organizations including DHL, Lufthansa, and Euromonitor, Caplena delivers transparent, explainable AI, customizable dashboards, and agentic workflows that helps teams move from unstructured feedback to world-class insights, fast.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 48

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.7/10 (Category avg: 8.1/10)
- **Compositionality:** 8.2/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.1/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Caplena ](https://www.g2.com/sellers/caplena)
- **Company Website:** https://www.caplena.com
- **Year Founded:** 2017
- **HQ Location:** Zürich, CH
- **Twitter:** @CaplenaCH (70 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/27224654/ (26 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Market Research
  - **Company Size:** 35% Mid-Market, 21% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (23 reviews)
- Customer Support (11 reviews)
- Categorization (10 reviews)
- AI Integration (9 reviews)
- AI Technology (9 reviews)

**Cons:**

- Not Intuitive (7 reviews)
- Missing Features (6 reviews)
- Limitations (5 reviews)
- Data Management (4 reviews)
- Lacking Features (4 reviews)

  ### 6. [Dovetail](https://www.g2.com/products/dovetail-research-pty-ltd-dovetail/reviews)
  It’s never been easier to build a product or service. The barriers to entry (ideas, talent, and tooling) are quickly becoming commoditized by AI. The faster your teams align behind and solve the most critical customer problems, the more revenue and market share you unlock. The only way to win is to identify what customers need and deliver it before the competition. But this is difficult to do. Data is scattered across teams and tools using various methods and it is difficult to understand, and align on, at speed. Even in the world of AI, the unique challenges associated with gathering, analyzing, and understanding complex customer feedback lead to teams wasting millions of dollars in failed products, slower development cycles, and duplicated efforts. As a result, they are continually risking decreases in customer satisfaction, and ultimately revenue. Dovetail provides always-on customer understanding. Our AI-native customer intelligence platform automatically turn sales calls, user feedback, support tickets, and voice of customer data into actionable insights that grow your business. Dovetail integrates with dozens of tools like Gong, Intercom, Zoom, Salesforce, Slack, Teams, and Google Play to analyze video, audio, documents, and text. Auto-generate reports and requirements documents; configure dashboards to visualize trends; and set up agents to ensure insights are acted on. Enable your team to track feature requests, identify pain points, reduce churn, and increase customer satisfaction through high-quality, accurate, and real-time customer intelligence that’s accessible to everyone. Deploy the industry-standard, enterprise-grade system of record for all of your customer intelligence. Put your customer first and grow your business. We’re for teams who care about solving real customer problems. Join the likes of Meta, Volvo, AWS, Dyson, Deloitte, and thousands more as they put their customer first with Dovetail.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 166

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **Custom Extension:** 4.5/10 (Category avg: 8.1/10)
- **Compositionality:** 5.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 5.1/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Dovetail Research Pty. Ltd.](https://www.g2.com/sellers/dovetail-research-pty-ltd)
- **Company Website:** https://dovetail.com/
- **Year Founded:** 2017
- **HQ Location:** Sydney, Australia
- **Twitter:** @hidovetail (2,181 Twitter followers)
- **LinkedIn® Page:** https://au.linkedin.com/company/heydovetail (169 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** UX Researcher, Senior UX Researcher
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 45% Mid-Market, 27% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (114 reviews)
- Features (83 reviews)
- Useful (51 reviews)
- Insights (50 reviews)
- Insights Analysis (48 reviews)

**Cons:**

- Missing Features (39 reviews)
- Limitations (35 reviews)
- Inefficient Tagging (28 reviews)
- Complexity (25 reviews)
- Feature Limitations (22 reviews)

  ### 7. [Kimola](https://www.g2.com/products/kimola/reviews)
  Kimola lets you scrape and collect feedback from 30+ channels, then analyze, classify, and summarize it all—from product reviews and survey responses to chats and call-center conversations. Whether it’s e-commerce reviews, CSAT responses, or support tickets, Kimola transforms raw feedback into structured insights you can act on. Trusted by clients across 90+ countries Trusted by clients in 90+ countries, 1000+ businesses, Kimola is used by global enterprises like P&G Singapore, Pizza Hut Spain, Michelin Brazil, Honda Netherlands, Costa Coffee UK, Lufthansa Airlines as well as growing SMBs including Plan3, Astropay and Blueberry Markets. Our users range from product and #CX teams to mobile applications, museums, restaurants, and even pilates studios—proving that understanding your customers matters in every industry. Here are TOP features why 1000+ companies choose Kimola: - Collect reviews and conversations across web, social media, mobile App Stores, e-commerce sites, Tripadvisor, Trustpilot, Google Business and more or upload your custom dataset: Your customers are talking everywhere. Kimola makes it easy to gather their voices from websites, social media, mobile app stores, e-commerce platforms, Intercom, Zendesk, and trusted sources like Tripadvisor, Trustpilot, and Google Business—all in one place. - Auto-Classify instantly and analyze themes with multi-labels & multi-sentiments: No need for prior AI training to analyze your reviews. Just upload your dataset and analyze reviews instantly with multi-aspects and multi-sentiments. Because all researchers will know that single labels won't work for the best insights! - Create Custom Models without even training - Create Summarizations No more sifting through thousands of reviews. Kimola automatically generates structured summaries—from feature requests and pain points to usage motivations and executive-ready reports—so you can take action faster. - Export reports to Powerpoint, PDF, Excel, CSV Easily share your findings across teams. Export your insights in PowerPoint, Excel, or CSV formats to plug directly into your reporting workflows. - Analyze in 30+ languages over 95,4% accuracy rate. Kimola analyzes customer feedback in over 30 languages with a very high accuracy rate, helping you understand your audience like never before.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.8/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.9/10 (Category avg: 8.1/10)
- **Compositionality:** 8.9/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 9.1/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Kimola](https://www.g2.com/sellers/kimola)
- **Year Founded:** 2014
- **HQ Location:** San Francisco, CALIFORNIA
- **Twitter:** @kimola101 (869 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kimola (10 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Marketing and Advertising, Information Technology and Services
  - **Company Size:** 59% Small-Business, 32% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (8 reviews)
- Accuracy (7 reviews)
- Insights Generation (5 reviews)
- Time-Saving (5 reviews)
- Customer Support (4 reviews)

**Cons:**

- Poor Interface Design (3 reviews)
- Lacking Features (2 reviews)
- Complex Setup (1 reviews)
- Email Issues (1 reviews)
- Exporting Limitations (1 reviews)

  ### 8. [Speak](https://www.g2.com/products/speak-ai-speak/reviews)
  Speak is a no-code transcription and natural language processing platform that helps researchers and marketers extract valuable insights from media. Get professional and automated transcription, generate dashboard reports and capture audio, video and text data at scale. Over 150,000+ individuals and teams from over 150 countries have signed up to easily integrate language analysis into workflows for breakthroughs in efficiency and intelligence. Get access to a 7-day trial with 2 hours of transcription and analysis and all features included.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 28

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 9.0/10)
- **Custom Extension:** 9.2/10 (Category avg: 8.1/10)
- **Compositionality:** 9.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.9/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Speak Ai](https://www.g2.com/sellers/speak-ai)
- **Year Founded:** 2019
- **HQ Location:** Toronto, CA
- **Twitter:** @speakai_co (258 Twitter followers)
- **LinkedIn® Page:** https://linkedin.com/company/speakai-co (6 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Non-Profit Organization Management
  - **Company Size:** 89% Small-Business, 7% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Time-saving (5 reviews)
- Transcription (5 reviews)
- Accuracy (4 reviews)
- Transcription Accuracy (4 reviews)

**Cons:**

- Cost (2 reviews)
- Subscription Issues (2 reviews)
- Accuracy Issues (1 reviews)
- Joining Issues (1 reviews)
- Language Limitations (1 reviews)

  ### 9. [Amazon Comprehend](https://www.g2.com/products/amazon-comprehend/reviews)
  Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 71

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.9/10 (Category avg: 8.1/10)
- **Compositionality:** 8.5/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.2/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,223,984 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 40% Mid-Market, 38% Small-Business


#### Pros & Cons

**Pros:**

- Access (1 reviews)
- Content Creation (1 reviews)
- Ease of Use (1 reviews)
- Insights (1 reviews)
- Insights Analysis (1 reviews)

**Cons:**

- Accuracy Issues (1 reviews)
- Expensive (1 reviews)
- Insufficient Training (1 reviews)

  ### 10. [Unwrap.ai](https://www.g2.com/products/unwrap-ai/reviews)
  At Unwrap, we're on a mission to help fill the world with products people love. Our customer intelligence platform integrates with all of your feedback sources (support tickets, reviews, surveys, and more), then proactively extracts patterns and trends from your feedback and surfaces them to you. With a deeper understanding of all your customers, Unwrap helps you build your product roadmap in confidence, and helps you prevent churn by shipping features users actually want.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 25

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.9/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [Unwrap.ai](https://www.g2.com/sellers/unwrap-ai)
- **Company Website:** https://unwrap.ai
- **Year Founded:** 2022
- **HQ Location:** Santa Barbara, California
- **Twitter:** @unwrapai (150 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/unwrapai/ (36 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 50% Mid-Market, 27% Enterprise


#### Pros & Cons

**Pros:**

- Helpful (3 reviews)
- Ease of Use (2 reviews)
- Feedback Management (2 reviews)
- Improvement (2 reviews)
- Time-Saving (2 reviews)

**Cons:**

- Integration Issues (1 reviews)
- Limitations (1 reviews)
- Search Functionality (1 reviews)

  ### 11. [IBM Watson Studio](https://www.g2.com/products/ibm-watson-studio/reviews)
  IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle--simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise. As a key offering of IBM Cloud Pak for Data, a unified data and AI platform, Watson Studio integrates seamlessly with data management services, data privacy and security capabilities, AI application tooling, open source frameworks, and a robust technology ecosystem. It unites teams and empowers businesses to build the modern information architecture that AI requires and infuse it across the organization. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same platform by providing the best of open source tools along with visual, drag-and-drop capabilities. It enables organizations to tap into data assets and inject predictions into business processes and modern applications—helping them maximize their business value. It's suited for hybrid multicloud environments that demand mission-critical performance, security, and governance. Features include: • AutoAI that eliminates time-consuming, repetitive tasks by automating data preparation, model development, feature engineering and hyperparameter optimization. • Text Analytics for uncovering insights from unstructured data • Drag-and-drop visual model-building with SPSS Modeler • Broad data access – flat files, spreadsheets, major relational databases • Sophisticated graphics engine for building stunning visualizations • Support for Python 3 Notebooks Watson Studio is available via several deployment options: • IBM Cloud Pak for Data – An open, extensible data and AI platform that runs on any cloud • IBM Cloud Pak for Data System – A hybrid cloud, on-premises platform-in-a-box • IBM Cloud Pak for Data as a Service – A set of IBM Cloud Pak for Data platform services fully managed on the IBM Cloud


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 160

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.0/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.1/10 (Category avg: 8.1/10)
- **Compositionality:** 9.2/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 9.0/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, CEO
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 50% Enterprise, 30% Small-Business


#### Pros & Cons

**Pros:**

- AI Capabilities (4 reviews)
- AI Technology (4 reviews)
- Ease of Use (4 reviews)
- Machine Learning (4 reviews)
- AI Integration (3 reviews)

**Cons:**

- Expensive (3 reviews)
- Learning Curve (3 reviews)
- Steep Learning Curve (3 reviews)
- Complex Interface (1 reviews)
- Complexity (1 reviews)

  ### 12. [SAP HANA Cloud](https://www.g2.com/products/sap-hana-cloud-2025-10-01/reviews)
  SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 509

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 9.0/10)
- **Custom Extension:** 9.2/10 (Category avg: 8.1/10)
- **Compositionality:** 8.9/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.3/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [SAP](https://www.g2.com/sellers/sap)
- **Company Website:** https://www.sap.com/
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,227 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Consultant, SAP Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 61% Enterprise, 26% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (55 reviews)
- Easy Integrations (41 reviews)
- Integrations (40 reviews)
- Speed (39 reviews)
- Scalability (35 reviews)

**Cons:**

- Complexity (33 reviews)
- Expensive (32 reviews)
- Learning Curve (30 reviews)
- Difficult Learning (28 reviews)
- Complex Setup (20 reviews)

  ### 13. [Thematic](https://www.g2.com/products/thematic/reviews)
  Thematic is the customer intelligence layer that turns survey responses, calls, tickets, and reviews into a single voice of customer truth for decisions across the business. One Trusted Foundation for Customer Intelligence Thematic connects feedback across surveys, tickets, calls, and reviews in one click: creating a single, unified stream of customer intelligence. Our enterprise-grade infrastructure ensures secure access and complete control over your data, so you can consolidate feedback from across geographies, languages, and channels without compromising on governance. Unlike traditional analytics tools that require you to define categories before analysis begins, Thematic's AI discovers and enriches themes as feedback comes in. You act on what customers actually say, not what you expected to find. And because our system preserves context over time, teams see what's changing—not just what's loud. This is customer intelligence grounded in trends and institutional memory, not static snapshots. Tailored Intelligence for Every Team Product, CX, Support, and Marketing all work from one trusted source of customer intelligence, but get insights tailored to their specific decisions. With Lenses, each team gets their own theme model, scores and recommended actions from the unified feedback stream. Product teams connect feedback to specific features and releases. Marketing teams surface customer language that informs positioning and messaging. Support teams identify effort drivers. CX teams track satisfaction trends. Everyone looks at the same customer reality, but through the precise lens they need to make confident decisions. Tailored Metrics take this further. Teams define their own outcome metrics—effort, satisfaction, trust, release quality—and Thematic's AI turns raw feedback into consistent, metric-like scores on any dataset. No manual tagging. No model training. Just structured intelligence aligned to what each team needs to measure. MCP connections, alerts and role-based dashboards make it easy to drive ownership and take action with the most important metrics, issues, and channels for each team. Instead of one generic dashboard that serves no one well, every function gets a view that surfaces what matters most to them. Need to go deeper? Deep Dive automatically identifies specific insights and trends that surface in themes, quantified and traced back to verified customer quotes. When leadership asks "how do you know this is accurate?" you have the evidence to defend your recommendations. Enterprise-Ready at Scale Thematic is built for enterprises managing millions of feedback records across multiple sources, regions, and languages. Our platform automatically creates focused, department-specific intelligence with strong governance controls to ensure consistency and compliance across your entire customer intelligence operation. Role-based access, audit trails, and enterprise security standards mean you can scale confidently without sacrificing control. Integration is seamless. Connect your existing tech stack—survey platforms, CRM, support tools, review sites—in one click. Thematic fits into how your teams already work, pushing insights directly into existing workflows to enable automatic loop-closing and immediate action. The Result Powerful customer intelligence that makes it easy to get a cohesive analysis of your qualitative and quantitative data across channels: what's happened, where, among who, and why. Enterprise customers typically see payback within three months, with one reporting 543% ROI over three years. As LendingTree discovered: "Thematic works straight out of the box," eliminating manual coding and model training. Trusted by Leading Enterprises Google, LinkedIn, DoorDash, Mitsubishi, AppFolio, Bill.com, Woolworths, K-Mart, Jetstar, NBN, Fonterra, and Atlassian trust Thematic to power customer intelligence across their organizations. Key Capabilities Connect feedback in one click: Unify surveys, tickets, calls, and reviews into one trusted source of customer intelligence with enterprise-grade security and data control \* Theme Discovery: Thematic AI builds themes directly from your feedback data and enriches them as new data comes in. Guide the AI with your business knowledge to get specific. It's fast, precise, and unbiased. \* Theme Lens editor: Each team builds their own analysis lens for their specific decisions while working from the same unified foundation \* Tailored Metrics: Define custom outcome metrics and let AI turn raw feedback into consistent scores on any dataset \* Deep Dive: Automatically surface specific insights and trends, quantified and traced to verified customer quotes \* Thematic Answers: Natural language interface that searches across all feedback sources for complete, consistent, source-linked insights \* Role-based dashboards: Drive ownership and action with dashboards that surface the most important metrics, issues, and channels for each team Turn Fragmented Feedback Into Your Competitive Advantage Use your customer feedback intelligently. With Thematic, every function gets the precise insights they need to make faster, more confident decisions. All from one trusted source of customer truth. Learn more at getthematic.com


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 43

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.9/10 (Category avg: 9.0/10)
- **Custom Extension:** 2.5/10 (Category avg: 8.1/10)
- **Compositionality:** 0.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 7.2/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Thematic](https://www.g2.com/sellers/thematic)
- **Company Website:** https://www.getthematic.com
- **Year Founded:** 2016
- **HQ Location:** San Francisco, CA
- **Twitter:** @getthematic (481 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/thematic-ltd/about (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 51% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Customer Support (4 reviews)
- Effective (3 reviews)
- Insights Generation (3 reviews)
- User Interface (3 reviews)

**Cons:**

- Complex Setup (1 reviews)
- Difficult Setup (1 reviews)
- Filtering Issues (1 reviews)
- Inaccuracy (1 reviews)
- Insufficient Information (1 reviews)

  ### 14. [ATLAS.ti](https://www.g2.com/products/atlas-ti/reviews)
  Leveraged by brands and academics alike, ATLAS.ti allows anyone to analyze data and uncover valuable insights – no matter which sector you work in. From basic analysis tasks to the most in-depth research projects: With ATLAS.ti, you can easily unlock actionable findings from your qualitative and mixed methods data with intuitive research tools and best-in-class technology: • Get access to native Mac and Win apps, plus our Web version • All features and tools included in one complete software package • Save time and find insights automatically, powered by AI • Experience seamless project exchange between versions • Take advantage of real-time collaboration for teams • Share multi-user licenses with as many people as you want • Benefit from our free live support and expert training Learn more here: www.atlasti.com


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 57

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.9/10 (Category avg: 8.1/10)
- **Compositionality:** 8.3/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 7.9/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [ATLAS.ti Scientific Software Development GmbH](https://www.g2.com/sellers/atlas-ti-scientific-software-development-gmbh)
- **Year Founded:** 1993
- **HQ Location:** Berlin
- **Twitter:** @ATLASti (4,206 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/5363207/ (50 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Higher Education, Research
  - **Company Size:** 37% Small-Business, 34% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (3 reviews)
- Categorization (2 reviews)
- Efficiency (2 reviews)
- Intuitive (2 reviews)
- Time-saving (2 reviews)

**Cons:**

- Data Inaccuracy (1 reviews)
- Error Handling (1 reviews)
- Limited Language Support (1 reviews)

  ### 15. [Dimension Labs](https://www.g2.com/products/dimension-labs/reviews)
  Dimension Labs' Chatbot AI Agent is an advanced analytics platform designed to enhance the performance of intent-based and generative AI chatbots. By providing AI-driven reporting and insights, it enables chatbot teams to optimize interactions, reduce drop-offs, and minimize human escalations, leading to more engaging and efficient customer experiences. Key Features and Functionality: - AI-Powered Analytics: Offers intelligent data insights to streamline bot training and ensure consistent, high-quality user interactions. - Conversation Visualization: Transforms unstructured customer conversations into structured data, allowing teams to uncover automation opportunities and improve chatbot performance. - Omni-Channel Dashboards: Monitors key customer experience metrics, such as Net Promoter Score (NPS) and customer satisfaction, in real-time across multiple data sources, enabling rapid identification and resolution of emerging issues. - Cost Reduction: Identifies patterns in customer interactions that influence business metrics, helping to reduce cost per interaction and escalation rates while improving NPS, Customer Satisfaction (CSAT), and Customer Effort Score (CES). Primary Value and User Solutions: Dimension Labs' Chatbot AI Agent empowers organizations to extract actionable insights from customer interactions, leading to significant improvements in chatbot efficiency and customer satisfaction. By automating the analysis of conversational data, it reduces the need for manual transcript reviews, accelerates bot training, and ensures a consistent, high-quality customer experience. This results in substantial cost savings, enhanced self-service workflows, and increased up-sell rates through optimized chatbot interactions.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 15


**Seller Details:**

- **Seller:** [Dimension Labs](https://www.g2.com/sellers/dimension-labs)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/dimensionlabsio (13 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 73% Mid-Market, 20% Small-Business


#### Pros & Cons

**Pros:**

- Reporting (4 reviews)
- Customer Support (3 reviews)
- Dashboard Design (3 reviews)
- Ease of Use (3 reviews)
- Insights (3 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Customization Issues (3 reviews)
- Limited Customization (3 reviews)
- Steep Learning Curve (3 reviews)
- Complexity (2 reviews)

  ### 16. [IBM Watson Natural Language Understanding](https://www.g2.com/products/ibm-watson-natural-language-understanding/reviews)
  Analyze text to extract meta-data from content such as concepts, entities, keywords, categories, relations and semantic roles.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 31

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 56% Small-Business, 26% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Accuracy (5 reviews)
- User Interface (4 reviews)
- Customization (3 reviews)
- Functionality (3 reviews)

**Cons:**

- Complex Setup (4 reviews)
- Limitations (2 reviews)
- Complexity (1 reviews)
- Difficult Learning (1 reviews)
- Not User-Friendly (1 reviews)

  ### 17. [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews)
  Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 490

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.7/10 (Category avg: 8.1/10)
- **Compositionality:** 8.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.3/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7)
- **Company Website:** https://www.altair.com/
- **Year Founded:** 1985
- **HQ Location:** Troy, MI
- **LinkedIn® Page:** https://www.linkedin.com/company/8323/ (3,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:ALTR

**Reviewer Demographics:**
  - **Who Uses This:** Student, Data Scientist
  - **Top Industries:** Higher Education, Education Management
  - **Company Size:** 43% Small-Business, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Machine Learning (8 reviews)
- AI Integration (6 reviews)
- AI Technology (5 reviews)
- Automation (5 reviews)

**Cons:**

- Complexity (4 reviews)
- Large Dataset Handling (3 reviews)
- Slow Performance (3 reviews)
- Complexity Issues (2 reviews)
- Complex Usage (2 reviews)

  ### 18. [IBM Watson Explorer](https://www.g2.com/products/ibm-watson-explorer/reviews)
  A smart, simple way to mine and explore all your unstructured data with powerful text analytics and machine learning Make a million documents as easy as one A large insurance company processed millions of claims a year. A complex claim that took 2 days to process can now be completed in 10 minutes with Watson Explorer. Unlock secrets hiding in your data A Global automotive manufacturer used Watson Explorer to identify defects faster, preventing expensive recalls and saving lives. Get results fast with cognitive assist A Global airline used Watson Explorer to enable a crew of over 2,000 to react more quickly to maintenance issues. With the help of IBM, over 200,000 cases per year are addressed 90 percent faster.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 25

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 63% Enterprise, 28% Mid-Market


  ### 19. [SAS Visual Text Analytics](https://www.g2.com/products/sas-visual-text-analytics/reviews)
  SAS Visual Text Analytics is a comprehensive solution designed to extract valuable insights from unstructured text data by leveraging natural language processing (NLP), machine learning, and linguistic rules. This powerful tool enables organizations to efficiently process large volumes of textual information, uncover hidden patterns, and make data-driven decisions. Key Features and Functionality: - Text Mining and Contextual Extraction: Automatically identify and extract key terms, phrases, and concepts from text data, facilitating a deeper understanding of the content. - Categorization and Sentiment Analysis: Classify documents into predefined categories and assess sentiment to gauge public opinion or customer feedback. - Topic Detection: Uncover emerging trends and hidden opportunities by detecting main ideas or topics within large text datasets. - Multilingual Support: Analyze text in 33 languages, including English, Spanish, Chinese, and Arabic, with built-in lexicons and stop lists for each language. - Open Integration: Seamlessly integrate with existing systems and open-source technologies, supporting various programming languages such as SAS, Python, R, Java, Scala, and Lua. - Automation and Collaboration: Utilize intelligent algorithms to automate the detection of relationships, topics, and sentiment, reducing manual analysis efforts. Foster collaboration by creating, managing, and sharing content in a highly collaborative workspace. Primary Value and User Solutions: SAS Visual Text Analytics empowers organizations to transform unstructured text data into actionable insights, addressing challenges such as managing and interpreting notes, assessing risk and fraud, and leveraging customer feedback for early problem detection. By automating the analysis process and providing a flexible, open environment, it enhances decision-making, improves operational efficiency, and uncovers opportunities hidden within vast amounts of textual information.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 57

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 9.0/10)
- **Custom Extension:** 8.0/10 (Category avg: 8.1/10)
- **Compositionality:** 8.3/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.1/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,996 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)
- **Phone:** 1-800-727-0025

**Reviewer Demographics:**
  - **Who Uses This:** Inside Sales Manager
  - **Top Industries:** Computer Software
  - **Company Size:** 75% Enterprise, 20% Small-Business


#### Pros & Cons

**Pros:**

- Modeling (1 reviews)
- Scalability (1 reviews)
- User Interface (1 reviews)


  ### 20. [Forsta](https://www.g2.com/products/forsta/reviews)
  Forsta, a Press Ganey company, powers the HX (Human Experience) Platform – a comprehensive Experience and Research Technology platform that breaks down the silos between CX (Customer Experience), Employee Experience (EX), Market Research – so that companies can get a deeper, more complete understanding of the experiences of their audiences. The HX Platform gathers and analyzes data, and translates the findings into shareable actions to inform decision-making and drive growth. Forsta’s technology, combined with its team of expert consultants, serves organizations across a variety of industries including financial services, healthcare, hospitality, market research, professional services, retail and technology. Forsta is recognized as a Leader in the 2021 Gartner® Magic Quadrant™ for Voice of the Customer. For more information, visit www.forsta.com.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 297

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 9.0/10)
- **Custom Extension:** 9.2/10 (Category avg: 8.1/10)
- **Compositionality:** 10.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.9/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Forsta](https://www.g2.com/sellers/forsta)
- **Year Founded:** 1990
- **HQ Location:** London, United Kingdom
- **Twitter:** @Forstaglobal (844 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/forstainfo/ (485 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Project Manager, Manager
  - **Top Industries:** Market Research, Management Consulting
  - **Company Size:** 43% Mid-Market, 29% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (5 reviews)
- Insights (4 reviews)
- Customization (3 reviews)
- Data Analysis (3 reviews)
- Efficiency Improvement (3 reviews)

**Cons:**

- Limited Options (3 reviews)
- Poor Performance (3 reviews)
- Time-Consuming (3 reviews)
- Delay Issues (2 reviews)
- Expensive (2 reviews)

  ### 21. [Kapiche](https://www.g2.com/products/kapiche/reviews)
  Kapiche is a feedback analytics platform that analyzes mountains of customer feedback in minutes, allowing you to provide deep insights quickly and help your company make better decisions. Our platform doesn't require any set-up or code framing. It just works, immediately. And it lets you analyze all your customer feedback in one place. With Kapiche, you can get to insights 30x faster. That means no more waiting weeks or months to get results; you'll be able to answer questions in real-time. Easily measure the impact of themes on CX metrics, drill down quickly to identify root causes, and be notified about new trends in customer feedback. Kapiche also helps you share insights across your organization with confidence. Your teams and leadership will have easy access to explore and collaborate on your customer insights. And you’ll love how you can quickly generate impressive reports and answer ad-hoc questions on the fly.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 42

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.1/10 (Category avg: 8.1/10)
- **Compositionality:** 8.0/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.3/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Kapiche](https://www.g2.com/sellers/kapiche)
- **Year Founded:** 2016
- **HQ Location:** Fortitude Valley, QLD
- **Twitter:** @kapicheofficial (253 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3832320/ (20 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Market Research
  - **Company Size:** 48% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Customer Insights (2 reviews)
- Ease of Use (2 reviews)
- Insights Generation (2 reviews)
- Automation (1 reviews)
- Categorization (1 reviews)

**Cons:**

- Accuracy Issues (1 reviews)
- Connection Issues (1 reviews)
- Data Management (1 reviews)
- Export Limitations (1 reviews)
- Insufficient Information (1 reviews)

  ### 22. [Google Cloud AutoML Natural Language](https://www.g2.com/products/google-cloud-automl-natural-language/reviews)
  The powerful pre-trained models of the Natural Language API let developers work with natural language understanding features including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 9.0/10)
- **Custom Extension:** 5.8/10 (Category avg: 8.1/10)
- **Compositionality:** 6.7/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 5.8/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,885,216 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Company Size:** 53% Small-Business, 27% Enterprise


  ### 23. [Microsoft Text Analytics API](https://www.g2.com/products/microsoft-text-analytics-api/reviews)
  Microsoft Text Analytics API is a suite of text analytics services that offer APIs for sentiment analysis, key phrase extraction and topic detection for English text, as well as language detection for 120 languages.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 6.7/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,105,844 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Company Size:** 57% Small-Business, 29% Mid-Market


  ### 24. [Enterpret](https://www.g2.com/products/enterpret-inc-enterpret/reviews)
  Enterpret empowers customer support, CX, and product teams to scale their understanding of customer feedback effortlessly. As your customer base grows and products become more complex, manually tagging and processing feedback quickly becomes unmanageable. Without comprehensive, trustworthy feedback data, product decisions often fall victim to recency bias or whoever speaks the loudest. Enterpret solves this challenge as a Unified Customer Feedback Intelligence platform, consolidating feedback from every critical channel—including Zendesk, Slack, Twitter, NPS surveys, app store reviews, and community forums—into a single source of truth. Leveraging advanced AI, Enterpret automatically categorizes and organizes feedback into a structured hierarchy, surfacing deep, actionable insights that authentically capture the Voice of the Customer. Teams rely on these insights to spot trends, enhance customer retention, drive revenue growth, prioritize effectively, and ensure alignment on the most impactful customer issues. Leading customer-centric companies like Canva, Notion, Strava, Hinge, and The Farmer's Dog use Enterpret to deliver exceptional customer experiences and fuel their growth through insightful feedback analysis.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 110

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 9.0/10)


**Seller Details:**

- **Seller:** [Enterpret](https://www.g2.com/sellers/enterpret-733ec72e-3cd6-4de8-990e-004c4a6e0c6a)
- **Company Website:** https://www.enterpret.com/
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @enterpret_ai (867 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/enterpret/ (66 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Product Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 57% Mid-Market, 31% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (42 reviews)
- Feedback Management (40 reviews)
- Insights Generation (36 reviews)
- Insights Analysis (25 reviews)
- Customer Support (18 reviews)

**Cons:**

- Integration Issues (10 reviews)
- Difficult Setup (9 reviews)
- Steep Learning Curve (9 reviews)
- Filtering Issues (8 reviews)
- Inaccuracy (8 reviews)

  ### 25. [Relative Insight](https://www.g2.com/products/relative-insight/reviews)
  Relative Insight delivers AI-powered text analysis to help brands and agencies generate customer, target audience and competitor intelligence from words. The platform delivers an efficient and scalable solution for uncovering actionable insights from survey open-ends, reviews, customer service transcripts and online conversations. Using technology originally developed in partnership with the British government to keep children safe online, our comparative approach reveals what makes audience groups and brands unique and similar to one another. Insights are being used by leading brands and agencies to inform marketing messaging, product development, customer service strategy and more. Quantitative analysis tells you WHAT is happening in your business, text analysis tells you WHY.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 57

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.0/10)
- **Custom Extension:** 7.5/10 (Category avg: 8.1/10)
- **Compositionality:** 6.7/10 (Category avg: 8.3/10)
- **Pre-Built Parameterization:** 8.3/10 (Category avg: 8.3/10)


**Seller Details:**

- **Seller:** [Relative Insight Ltd](https://www.g2.com/sellers/relative-insight-ltd)
- **Year Founded:** 2012
- **HQ Location:** London, England
- **Twitter:** @RelativeInsight (797 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/5111062/ (25 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Marketing and Advertising, Market Research
  - **Company Size:** 40% Small-Business, 30% Mid-Market




## Parent Category

[Analytics Tools & Software](https://www.g2.com/categories/analytics-tools-software)



## Related Categories

- [Enterprise Feedback Management Software](https://www.g2.com/categories/enterprise-feedback-management)
- [Analytics Platforms](https://www.g2.com/categories/analytics-platforms)
- [Feedback Analytics Software](https://www.g2.com/categories/feedback-analytics)



---

## Buyer Guide

### What You Should Know About Text Analysis Software

### What is Text Analysis Software?

Text analysis software helps businesses analyze their text data using natural language understanding, which is a subset of natural language processing. Because of the unstructured nature of text data, these analytics solutions take text as an input and provide some form of labels, tags, or insights as an input. In the age of digital transformation, businesses are embracing the need to understand company data like never before. 

Text analysis software, also known as text mining software or text analytics software, has become an important tool for nearly every business over the past decade. A more recent aspect of analytics and business intelligence is the need to understand not just structured data, but unstructured data as well. Unstructured data, such as text data, can be mined for meaning to inform business decisions. 

Text mining initiatives can help businesses ultimately better understand textual data sets. Being able to pull out actionable insights from numerical data housed in [ERP](https://www.g2.com/categories/erp) systems, [CRM software](https://www.g2.com/categories/crm), or [accounting software](https://www.g2.com/categories/accounting) is one thing, but being able to gain insights from unstructured data sources is invaluable. Without dedicated software for this task, businesses must either spend significant time and resources on building natural language understanding models or haphazardly investigating the data.

#### What Types of Text Analysis Software Exist?

Many types of text analysis solutions share overlapping functionality, while simultaneously catering to different user personas like data analysts and financial analysts, or providing unique services.

Some solutions may offer self-service features so that the average employee can assemble their charts and graphs from big data sets. Others, however, require more significant support from IT or data analysts.

**Self-service text analysis tools**

Self-service text analysis tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other non-data team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for tagging text, prebuilt templates for querying data, and other tools for data discovery. Similar to [analytics platforms](https://www.g2.com/categories/analytics-platforms), organizations use these tools to build interactive dashboards for discovering actionable insights.

For example, a customer service business leader might use this type of software to analyze thousands of customer emails to discover trends, such as sentiment and the choice of words they used. This analysis can inform how customer service agents respond to customers to achieve desired outcomes.

**Traditional text analysis tools**

As opposed to self-service options, some text analysis solutions are geared towards data professionals, such as data analysts and data scientists. They can use this software to train and deploy algorithms, as it assists them in tagging their data. Data scientists can use these tools to ingest text data, such as social media, call center transcriptions, news sources, and reviews, and to build and improve applications, achieving goals such as improving fraud detection and conducting sentiment analysis.

### What are the Common Features of Text Analysis Software?

Many capabilities of text analysis software can help users pull business-critical insights from text data.

**Language identification:** Text analytics solutions provide users with the ability to understand which language the text was written in. This can be beneficial when determining where a social media post came from or when a business has offices in multiple countries.

**Part of speech tagging:** Once the language is identified, text analysis software can tag each word with a part of speech, signifying if the word is a noun, verb, adjective, and so on.

**Syntax parsing:** Syntax parsing is very similar to part of speech tagging, but instead of understanding each word, it helps break down how a sentence was constructed and why.

**Entity recognition:** Text analytics solutions can help determine not just parts of speech but actual entities. For example, the part of speech may be a noun, but text analytics will break down whether that noun is a person or a place.

**Keyphrase extraction:** Another major feature of text mining and text analytics is keyphrase extraction, which allows users to determine patterns and themes within the text. These tools can pull out those common themes for the user.

**Sentiment analysis:** All of the above features can be relevant for sentiment analysis. Text analysis tools can offer up sentiment analysis scores, determining if the text is positive, negative, happy, sad, or neutral, among many other classifications. With the sentiment determined, businesses can decide how they want to act or interact with this data. For example, if a software company sees that all of their negative reviews are mentioning one particular feature, it might be a good idea to examine the state or viability of that feature.

### What are the Benefits of Text Analysis Software?

The reason to use text analysis software is rather straightforward—users need to analyze text—but there are many reasons behind why a business may want to perform text mining and analysis. It all boils down to better understanding and utilizing company data to impact business processes and the bottom line. It should be used to increase efficiency and productivity and to optimize processes that could be working better.

**Sentiment understanding:** Businesses are always trying to gauge customer satisfaction, and text analytics is an easy way to do so. Many different text data sources can provide customer sentiments, such as social media, emails from customers, phone transcripts, customer reviews, and others. If a company can understand their shortcomings or where they are excelling with customers, they can better support and manage those customers. Ultimately this can lead to increased revenue.

**Employee satisfaction:** Similarly to better understanding customers, businesses can improve employee engagement and satisfaction by using text analysis. While businesses shouldn’t necessarily spy on their employees, they can figure out employee sentiment and satisfaction based on surveys, emails, or phone transcripts. This can help businesses ensure that they are promoting the right company culture and providing a healthy and happy place to work.

**Survey analysis:** Text analysis is very often used when companies are running surveys. These surveys may be intended for customers or employees but can also relate to market research. Being able to quickly pull insights verbatim from survey responses can provide a unique perspective and insight that businesses may not be able to obtain through multiple-choice questions.

**Document classification:** An easy use case for text analysis software is document classification. Businesses often need to organize existing documents; by pulling out sentiment and themes, it can be much easier to bucket documents, such as invoices and contracts.

### Who Uses Text Analysis Software?

The typical user of text analytics is the same person who is tasked with using analytics and business intelligence solutions—a data analyst or data scientist. These users are trained in developing analytical and machine learning models used to pull out actionable insights from data. Data scientists are also tasked with deriving a business narrative from data, and text data is no different. If the text analytics product is of the self-service variety, less technical business users, such as operations, customer service, and finance teams can benefit from the technology to dig into their text data and derive insights. 

**Data analysts:** Depending on the complexity of the software, analysts may be required. They can help set up the requisite tagging of the text data and dashboards for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data. 

**Operations and supply chain teams:** A company’s supply chain frequently has many touchpoints, and as a result, many data points. Everything from invoices to shipping information can be analyzed with this software. Therefore, employees working in operations and supply chain teams can use text analysis software to gain a better understanding of their departments and the text data that is generated, such as from [ERP systems](https://www.g2.com/categories/erp). These applications track everything from accounting to supply chain and distribution. By inputting supply chain data into this software, supply chain managers can optimize several processes to save time and resources.

**Finance teams:** Finance teams leverage text analysis software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as [accounting software](https://www.g2.com/categories/accounting), employees such as chief financial officers (CFOs) can see how well the business is performing. For example, they can analyze free-text data in expense reports to discover trends in the data. With this knowledge, they can determine the biggest spenders and spending categories and put a plan in place to curb spending, if desired.

**Sales and marketing teams:** Sales teams also seek to improve financial metrics and can benefit tremendously from being more data driven. They can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. Through the analysis of survey data, business leaders can find out the most effective way to sell products.

For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, these tools allow marketing teams to track the performance of those campaigns in one central location. Marketers can learn about how their audience is responding to their messages using sentiment analysis. In addition, they can evaluate their ad copy by tagging and classifying it to better understand what drives conversions.

**Consultants:** Businesses do not always have the luxury to build, develop, and optimize their analytics solutions. Some businesses opt to employ external consultants, such as [business intelligence (BI) consulting providers](https://www.g2.com/categories/business-intelligence-bi-consulting). These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields. 

**Customer service teams:** Customer service teams are faced with a challenge. They are frequently inundated with a flurry of customer concerns, whether that be via text, voice, or mail. Although agents can respond to each comment and concern individually, it is beneficial to have a proper understanding of trends, including the sentiment of messages, the types of complaints, and more. Using text analysis software, businesses can equip their agents with tools to help them respond to messages in a targeted manner, depending on factors such as sentiment and key phrases.

### What are the Alternatives to Text Analysis Software?

Alternatives to text analysis software can replace this type of software, either partially or completely:

[Feedback analytics software](https://www.g2.com/categories/feedback-analytics) **:** Text analysis software is an all-purpose solution built to analyze any text data. Businesses looking to focus on feedback text, such as from surveys, review sites, social media, and customer service tools, can leverage feedback analytics software to achieve this goal. This software enables businesses to consolidate and analyze their customer feedback within a single platform.

#### Software Related to Text Analysis Software

Related solutions that can be used together with text analysis software include:

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** A key software necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using text analysis tools. Some text analysis platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.

[Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Analytics platforms might include some limited text analysis features, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.

[Stream analytics software](https://www.g2.com/categories/stream-analytics) **:** When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with the internet of things (IoT) data, which people usually want to analyze in real time.

[Predictive analytics software](https://www.g2.com/categories/predictive-analytics): Broad-purpose text analysis software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution. 

### Challenges with Text Analysis Software

Software solutions can come with their own set of challenges. 

**Need for skilled employees:** The main issue with text analysis software is that, despite the tool pulling information surrounding text data, it still requires a human to go that extra mile and determine what the data means. Without context, sentiment analysis, phrase tagging, and pulling themes or patterns from a text can only inform a user so much. An analyst will need to interpret that data and decipher the business implications of it. 

This is much more easily tackled with text analysis software because of the ability to visualize the data in an organized manner, but it still requires interpretation nonetheless. Some text analytics tools may offer a certain level of predictive analytics and provide users with suggestions or recommendations based on the data, but more often than not, human intervention is necessary.

**Data preparation:** Another potential concern is preparing the data to be ingested by the text analysis tool. The data needs to be stored properly, whether that is in a database or data warehouse and may require IT or a dedicated admin to ensure the text analytics tool can consume the data. The beauty of text analysis software is that it doesn’t always require the neatness of structured data. Unstructured data does not need to follow a columnar approach that structured data often requires.

**User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.

### Which Companies Should Buy Text Analysis Software?

As it has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and industries should consider some sort of analytics solution, such as text analysis to make sense of that data and begin to make data-driven decisions. Here are some illustrative examples of how textual analysis can be used in several industries:

**Financial services:** Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes.

**Healthcare:** Within the space of healthcare, bad data practices might have dire or even deadly consequences. Text analysis software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter.

**Retail** : Retail organizations, whether they’re B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With Text analysis software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions.

### How to Buy Text Analysis Software

#### Requirements Gathering (RFI/RFP) for Text Analysis Software

If a company is just starting out on its analytics journey, G2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2.com is a great place to sort and filter reviews based on these criteria, along with many more. The variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs and their future needs as they accumulate more data. 

To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a text analysis software.

#### Compare Text Analysis Software Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

**Conduct demos**

To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. 

#### Selection of Text Analysis Software

**Choose a selection team**

As text analysis software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes, facts, and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

**Negotiation**

Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Does Text Analysis Software Cost?

Businesses decide to deploy text analysis software to derive some degree of a return on investment (ROI).

#### Return on Investment (ROI)

As businesses look to recoup the funds they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate their gains from their use of the text analysis software.

### Implementation of Text Analysis Software

**How is Text Analysis Software Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether it’s an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is Responsible for Text Analysis Software Implementation?**

It may require a lot of people, or even teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together its data and begin the journey of analytics, starting with proper data preparation and management.

### Text Analysis Software Trends

**Data literacy**

Business data is no longer locked up in silos. With text analysis solutions, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) software](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data easier and more powerful, providing more accurate results. Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from all their various sources.

**Shift to the cloud**

The move from on-premises data analytics to the cloud has been underway for several years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons like time to insights. The move away from on-premises infrastructure has helped many companies enable data to work anywhere one has access to the cloud—anywhere with internet access. 

**Deep learning**

The main trend related to text analysis software is deep learning, but more specifically, natural language processing. As AI technology continues to advance, deep learning and NLP become more precise and effective when performing actions such as text analysis. This means that users need to do less digging through text, and instead, the insights are given to them. This is extremely beneficial, because, despite the comprehensive features that text analysis software provides, analysts are still required to dig through the data and determine the insights themselves. The next step, which NLP is contributing to, is to have the software provide actionable insights without the need to dig through the text data.