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 systems, CRM software, or accounting software 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, 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.

Page 2 | Best Text Analysis Software: User Reviews from September 2026

Thematic

Enterprise CX teams were promised that AI would turn customer feedback into decisions. Instead they got static taxonomies that drift, black-box AI they can't defend, and a different version of the customer story in every channel. AI agents make this worse. Built on fragmented data, they scale the inconsistency and risk. Thematic is the customer intelligence layer that fixes the foundation. We unify surveys, tickets, calls, reviews, and social into one source of customer truth, with every theme traceable back to the raw comments behind it. The same grounded layer feeds your dashboards, your teams, and the AI agents acting on customer signals. When teams are able to understand and act on data, the outcomes are measurable. Time-to-insight drops from days to minutes. We consistently see NPS gains of 5 to 35 points, which compounds into retention and referral revenue. A top-10 grocery retailer drove $4.8M in attributable revenue from a single 6-month pilot. A digital bank cut call volumes on their top three contact reasons by 69%. Forrester measured a 543% three-year ROI with payback under six months. Teams go from AI summaries they can't verify, or rigid text analytics buried in their CX platform, to insights they can stake a decision on. We sit on top of the existing CX stack (Medallia, Qualtrics, Snowflake, Gemini) and integrate via MCP with AI models. Up and running in days, not quarters. That’s why the world’s most innovative CX programs trust us, including DoorDash, Albertsons, and Mitsubishi.

Average Rating: 4.8/5.0

Total Reviews: 43

How Do G2 Users Rate Thematic?

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: 8.2/10)
Compositionality: 0.0/10 (Category avg: 8.3/10)
Pre-Built Parameterization: 7.2/10 (Category avg: 8.3/10)

Who Is the Company Behind Thematic?

Seller: Thematic
Company Website:
Year Founded: 2016
HQ Location: San Francisco, CA
Twitter: @getthematic
480 Twitter followers
LinkedIn® Page: www.linkedin.com
45 employees on LinkedIn®

Who Uses This Product?

Company Size: 51% Large, 30% Medium

What Do G2 Reviewers Say About Thematic?

AI-generated summary from verified user reviews

Pros

Users find Thematic's ease of use impressive, enjoying its intuitive interface and efficient data summarization.
Users praise the excellent customer support of Thematic, highlighting their helpfulness and accessibility at all times.
Users appreciate Thematic for its fast data summaries and intuitive interface, enabling quick insights and actionable improvements.
Users appreciate the speedy insights generation of Thematic, enabling timely analysis and informed decision-making.
Users value Thematic's easy-to-use interface, praising its intuitiveness and quick access to valuable insights.

Cons

Users find the complex setup of Thematic time-consuming and challenging when integrating multiple data sources.
Users find the difficult setup of Thematic to be time-consuming and complicated for integrating multiple data sources.
Users experience filtering issues, needing to reset filters every time they change the data set for analysis.
Users find Thematic's scoring mechanisms confusing, making it difficult to understand the impact of their actions.
Users find Thematic’s scoring mechanisms confusing, particularly the Impact score, complicating discussions around actionable insights.

What Are Recent G2 Reviews of Thematic?

"Fast, Flexible Data Summaries That Save Time"

Rating: 4.5/5.0 stars

— Lynn S.

Best Text Analysis Software - Page 2

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