Check out our list of free Text Analysis Software. Products featured on this list are the ones that offer a free trial version. As with most free versions, there are limitations, typically time or features.
If you'd like to see more products and to evaluate additional feature options, compare all Text Analysis Software to ensure you get the right product.
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment.
Chattermill is an AI-powered platform that helps companies drive customer loyalty and growth. With Chattermill you can take unstructured customer feedback from a variety of sources and generate clear and actionable insights in real-time. Customer Experience employees leverage our platform to identify themes and sentiment in each piece of customer feedback with human-level accuracy. AI-based insights help track how customers feel about their experiences identifying the areas that impact customer
Thematic analyzes customer feedback to help companies improve NPS, churn, loyalty and other metrics. We pull data from the systems you already use for surveys, social, contact center, and collect your public feedback. Within minutes, your data is analyzed in a consistent way: Our AI discovers specific and actionable themes bottoms-up, and adopts to changes in your feedback. You can easily edit the themes. Our purpose-built reporting & visualizations provide clear visual answer to your questi
When it comes to understanding language, it's all relative. With a heritage in protecting children from criminals online, the Relative Insight platform helps brands and agencies get business value out of language data, through comparison. By helping you discover the key differences and similarities between language sets, Relative Insight can help you analyse qualitative data at scale to gain valuable market and audience insights. Relative Insight layers on top of your existing language data an
As your conversation volume grows, it becomes hard to stay on top of customer issues and requests. The Prodsight app makes this easy by automatically analysing your Zendesk and Intercom conversations for topics and sentiment and producing a continuously updated report on the most common user issues. As a Customer Support Manager, you can use the Prodsight app to stay on top of customer issues and reduce support ticket volumes by writing well-informed help articles. As a Product Manager, yo
DeepOpinion Studio is the leading platform for Opinion Mining and Sentiment Analysis (ABSA). Our ever-growing library of industry-specific, pre-trained models enables all kinds of users to analyze open text feedback and reviews in human-level accuracy.
You don’t have to be a data scientist to work with the latest AI-driven analysis. Get actionable insights from hundreds of thousands of feedback texts in minutes. Explore what all your customers are saying in their own language. Upload your texts in Excel or CSV format, or scrape online reviews about your company or a competitor - then get results immediately. Results are presented in interactive dashboards that can be shared with clients or stakeholders. Explorer can identify and group words
PolyAnalyst and PolyAnalyst for Text is the leading system for extracting actionable knowledge hidden in piles of free text and structured data. Whatever your data source, challenge, or skill level, PolyAnalyst is the tool of choice for turning data into valuable business insight. - Access nearly any source of data, and merge data from different sources - Whip dirty data into shape with powerful data cleansing operations, such as imputing missing values and correcting spelling errors - Choose f
teX.ai is one of the leading Ai based Text Analytics product. It’s completely customizable and helps convert complex text data into accurate insights. teX.ai is a hands-on, easy to use text analytics tool built on sophisticated Python libraries. This SaaS based text analytics suite provides insights to enhance customer experience by processing raw text data using NLP, Ai and DL algorithms. It can effectively solve challenges faced by industries Banking, Retail, Ecommerce, Manufacturing, Educat
A professional survey solution that perfectly understands text answers. Unique language capabilities to analyse text data instantly with human-level precision.
In the age of digital transformation, businesses are embracing the need to understand company data like never before. Analytics software has become a steady initiative for nearly every CIO 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. This means being able to perform text mining initiatives, by implementing text analysis software, to ultimately better understand textual data sets. Being able to pull out actionable insights from numerical data housed in ERP systems, CRMs, or accounting software is one thing, but being able to understand text data, sentiment analysis, and other insights from unstructured data sources is invaluable.
Key Benefits of Text Analysis Software
The reason to use text analysis software is rather straightforward—you 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.
Understand Customer Sentiment — Businesses are always trying to gauge customer satisfaction, and text analytics is an easy way to do so. There are many different text data sources that can provide customer sentiment, 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.
Understand Employee Sentiment — 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.
Easy Survey Analysis — Text analytics 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.
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.
Some other potential users of text analysis software are social media managers looking for insights, sales and customer service managers tasked with understanding customer sentiment, or human resources managers interested in gauging employee satisfaction. However, most of those end users will likely need assistance from a data analyst or IT administrator.
There are a number of capabilities of text analysis software that 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 to 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 text. These tools can pull out those common themes for the user.
Sentiment Analysis — All of these features ultimately lead to sentiment analysis. Text analytics tools can offer up sentiment analysis scores, determining if the text is positive, negative, happy, sad, or neutral, among many other classifications.
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 really mean. Without context, sentiment analysis, phrase tagging, and pulling themes or patterns from 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 analytics 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.
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 require.