Best Text Analytics Software allows users to perform many text analytics functions from open-ended analysis of a document or email to a more focused investigation of a specific topic. Users can instantly tap into big data repositories including the Internet, SCIFs and classified systems using natural language processing to deliver results
Discover which software is the Best Text Analytics Software in the industry. See top ranked products and compare features, pricing, performance and more.
SAS
SAS is a software tool that aims to help you get actionable insights from unstructured data, especially online content, ranging from books to comment forms. Because this software also lets you guide the machine learning process, you can narrow down automatically generated topics and rules. You can also see how the rules change over time, and thus refine your approach for better results.
Thematic
Thematic is designed to analyze customer feedback using artificial intelligence, i.e., natural language processing and deep learning.
The platform offers three AI tools: Thematic Intelligence, Thematic Insights, and Thematic Catalyst. Thematic Intelligence deals with the actual extraction of meaning in texts, grouping content into themes, Thematic Insights delivers results relating to trends and patterns in your themes, and Thematic Catalyst enables you to create data visualizations.
Thematic works seamlessly with tools you already use – SurveyMonkey, Zendesk, internal databases, or any NPS provider you use – so you can set up a working model in less than a day.
QDA Miner’s WordStat
QDA Miner has a range of capabilities for analyzing qualitative data. For text analysis, the program uses the WordStat add-on module. The module is designed for content analysis, text analysis, and sentiment analysis. It can be used to analyze websites and social media, as well as for business intelligence. There are a few visualization tools to help you better interpret the results from the program. WordStat’s correspondence analysis helps the program identify concepts and categories that are in your text.
Microsoft’s Cognitive Services suite
Cognitive Services offers a robust set of artificial intelligence tools that help you build intelligent apps with natural and contextual interaction. Rather than strictly a text analytics program, Cognitive Services incorporates elements of text analytics in how it analyzes speech and language. One of these is the Language Understanding intelligent service, which is designed to help bots and applications understand human input and communicate with people in natural language.
Google Cloud NLP
Not one to be left behind, Google’s solution to the growing text analysis trend is delivered in the form of Google Cloud NLP. This tool helps businesses find meaning in unstructured text and gain insights.
Google Cloud NLP focuses on different text analysis applications, such as entity extraction, syntax analysis, sentiment analysis, and content classification. If you’re keen to train your own machine learning models, all you’ll need is some training data to fine-tune your models to your domain-specific keywords, sentiments, and topics, etc.
One last perk! You can integrate Google’s AI tools with your Cloud Storage, creating a seamless text analysis experience.
Rocket Enterprise Search and Text Analytics
Security is an essential concern for companies dealing with large amounts of data, and Rocket’s tool takes this into account with their text analytic solution. Another feature of it is that it’s user-friendly, so teams can find the information they need quickly and easily. If you’re working with teams with limited technological experience, this tool can be very useful.
Voyant Tools
This application can be used by anyone as a text analytic tool for websites, though it tends to be popular for researchers in digital humanities. While not an in-depth text analytic system, Voyant Tools has a simple interface and the capabilities to do a variety of analytical tasks. In a matter of seconds, it can analyze a website and generate a visualization of the data in the text.
IBM Watson
Tech giant IBM offers a collection of AI tools that extract and classify information within structured or unstructured text data. Useful tools include IBM Watson Natural Language Understanding & Classifier, Watson Personality Insights, and the Watson Tone Analyzer.
IBM Watson Natural Language Understanding extracts concepts, entities, keywords, and categories, to name a few. When used to perform sentiment analysis, it not only sorts text into generic sentiment buckets – positive, negative, and neutral – it also sorts these sentiments by distinct emotions, such as confused, excited, sad, confident, etc.
The IBM Watson Natural Language Classifier enables developers to extract meaning from text and assign a classification – without needing to be an expert in machine learning or statistical algorithms. Developers can create their custom machine learning model by uploading their data, and let the model classify texts, extract insights, and identify trends.
IBM Watson Personality Insights is an industry favorite for its ease of use and focus on understanding customers’ personality traits. Models include:
- Big Five. Describes how a person engages with the world by focusing on five dimensions that help characterize an individual.
- Needs. Describes which aspects of a product resonate with a person by identifying characteristics such as excitement, curiosity, self-expression, challenge, etc.
- Values. Describes motivating factors that influence a person’s decision making, from self-transcendence/helping others to self-enhancement/achieving success.
Finally, the Watson Tone Analyzer uses linguistic analysis to detect emotion (happy, sad, angry, scared, etc) tendencies (openness, conscientiousness, extroversion, agreeableness, emotional range), and language style (confident, hesitant, analytical, assertive, etc).
You may remember Watson as being the computer that famously beat Jeopardy! star Ken Jennings, so it may not be a surprise that IBM’s computer system has a top-notch text analytics system, too. It’s called Watson Natural Language Understanding, and uses cognitive technology to analyze text, which includes assessing sentiment and emotions.
MonkeyLearn
MonkeyLearn is an easy-to-use, yet powerful, machine learning tool that focuses on automatically analyzing text and extracting actionable insights from data. You can use pre-trained text analysis models or create your own – and tailor them to your needs for higher levels of accuracy.
Text analysis models include text classifiers and text extractors, giving you the opportunity to perform sentiment analysis, keyword extraction, intent classification, language detection, and much more, in a matter of seconds.
Don’t worry if you’re new to text analysis tools. MonkeyLearn is a low-code, no-code machine learning platform, with a point and click interface, making it easy to create your own text analysis tools. And you’ll find key integrations with everyday apps like Excel, Google Sheets, Zapier, RapidMiner, and Zendesk, giving you direct access to your data.
For those who know how to code, MonkeyLearn API is available in all major programming languages, so you can seamlessly connect MonkeyLearn’s text analysis models to the tools you already use.
Open Calais
Open Calais is a cloud-based tool that helps you tag content. This tool’s strong point is recognizing relationships between different entities in unstructured data, and organizing them accordingly. While it can’t do complex sentiment analysis, it can help you deal with unstructured data and turn it into a well-organized knowledge base.
Conclusion
Discover the best-rated text analytics software based on an independent study of market data that includes solution size, user, and company satisfaction. The report also contains competitive benchmarking data for this category across verticals.