Text mining utilizes natural language processing and artificial intelligence algorithms to detect patterns, trends, and key information in a large volume of text data. It focuses on unstructured data in common documents like text messages, books, websites, research papers, emails, product descriptions, online reviews, survey responses, and more.
In general, text mining processes include text clustering, categorization, creation of granular taxonomies, sentiment analysis, and concept extraction. It also involves determining relationships among two or more named entities.
But how is it useful to businesses?
It is estimated that about 80% of business data resides in an unstructured format. Text mining allows companies to extract valuable information from massive volumes of unstructured data. Since the process is automated, it can save a lot of time and effort (and thus, professionals can focus on what matters the most — revenue growth and customer experience).
By using text mining software, businesses can identify problems and act quickly. Modern tools allow businesses across various industries to anticipate competitive threats, reduce risk, improve customer service, and monitor important metrics like employee satisfaction.
How to implement a text mining solution?
Now that you know the importance of text mining software, the next thing is to decide whether you want to develop or purchase a text mining solution. Building a software program would take time — even if you are an experienced programmer, you will need to hire developers and spend months building tools.
The more efficient solution would be selecting a SaaS service (cloud-based tool) or an open-source tool (like KNIME or Refinitiv). Below, we have listed the best text mining software platforms that help you extract and analyze large volumes of structured and unstructured text from various sources.
Released in 2021
Price: Based on project nature and size | Free demo available
Rating: 8.5/10 from 400+ customers
Unlike CRM systems and Chat logs, which never reveal the whole story, Forsta offers an effective text analytics platform to help businesses understand their whole customer journey.
It puts all data in one place, making it easier to organize, structure, and categorize texts generated from various sources. For example, the analytics can sort messages by language, order emails by subjects, and prioritize issues based on the severity and number of reviews.
It has 12-emotion segmentation to detect certain emotions and get detailed insights into documents. The platform uses both custom-rule-based and AI-powered tools to analyze surveys, feedback, and online conversations from any audience (from small private groups to worldwide communities).
- Put all data in one centralized platform
- Granular sentiment analysis
- Interactive dashboard
- Get visual answers to your questions
Overall, Forsta’s technology brings better data with deeper insights, so you can grow your business more efficiently. It has helped over 2,500 companies across a wide range of industries, including retail, technology, hospitality, market research, and financial services.
Released in 2014
Price: Starts at $299 per month | 14-day free trial available
Rating: 8.8/10 from 100+ customers
MonkeyLearn gives you an intuitive graphical interface to extract and analyze unstructured data. It is integrated with several machine learning models like keyword extraction, topic detection, sentiment analysis, and more.
The platform is pretty simple to use. First, connect your text data (emails, reviews, surveys, support tickets, etc.) by integrating your apps via API. Or you can simply upload CSV/Excel files.
Select from predesigned models or create your own custom classifiers and extractors. Classifiers can classify text into defined categories (topics, sentiment, priority, intent, etc.), and extractors can extract key metrics from the text (names, dates, prices, features, etc.).
Then, utilize MonkeyLearn tags to develop new workflows in your apps and to create new information about your business. You can even connect visualization tools like Google Data Studio and Tableau to get insights.
- Instant data visualization
- Pre-built templates tailored for various business scenarios
- Build custom topic and sentiment classifiers
- Integrates with hundreds of apps
The platform is specifically designed for analyzing customer reviews, open-ended feedback, historical and incoming tickets, surveys, and customer conversations.
Small businesses can start with the basic version, which supports up to three active users and 10,000 queries per month.
Released in 2015
Price: Based on the number of volumes received | Free trial available
Rating: 8.5/10 from 600+ customers
Chattermill is more than just a text mining and analysis tool — It’s a unified customer intelligence platform that puts large volumes of unstructured text in one place and uses machine learning techniques to analyze data and provide actionable insights.
More specifically, it unifies and analyzes social media, customer feedback, and support conversation data to give you the intelligence you need to deliver excellent customer experiences.
While its intuitive Dashboard lets you compare various metrics and time periods, Graphs helps you visualize complex data in a series of interactive charts. You can monitor almost everything, from hard-to-find patterns to granular differences between Google Play and App Store reviews.
- Automatic sentiment and intent analysis
- Track and visualize key metrics
- Identify emerging trends faster
- API access to manage large businesses
Overall, the platform makes it easier to understand what exactly you need to improve your products, operations, and customer services and how to deliver better, more customer-centric experiences.
Well-known companies like Zendesk, Uber, H&M, and JustEat use Chattermill to enhance their customer experience and grow their businesses.
If you are interested, you can start with a basic plan that allows you to analyze up to 1,000 pieces of feedback per month through 1 channel. Large businesses can opt for the enterprise plan, which includes more advanced features and fewer limitations with customized pricing models.
Released in 1994
Price: Based on the project size | 30-day free trial available
Rating: 8.2/10 from 100+ customers
PolyAnalyst features a balanced combination of statistics, natural language processing, and machine learning technologies to provide the most accurate results. It can handle all conventional text analysis tasks, such as extracting key values from textual contents and performing classification, summarization, and sentiment analysis.
The platform is quite customizable — it supports all the latest machine learning models and allows you to execute code in R and Python. Plus, it can process unstructured documents in 16 different languages (including Chinese, Japanese, German, Spanish, and French).
Advanced users can create multi-step data analysis scenarios for specific business needs. The best part is you don’t need any coding skills to do that. Everything can be done and managed via a simple graphical user interface.
- Merge data from different sources
- Effective data cleansing and manipulation tools
- Research literature analysis
- Generate browser-based interactive reports
All in all, PolyAnalyst provides a wide range of advanced text analytics capabilities to data scientists and engineers. It is well-recognized by industry analysts like Forrester and Gartner and used by many Fortune 500 companies.
You can test the Professional version (called PolyPro) for 30 days. It comes with both text analytics and predictive analytics, as well as a fully integrated reporting module.
5. Wolfram Mathematica
Released in 1988
Price: Starts at $194 per month | 15-day free trial available
Rating: 8.9/10 from 600+ customers
Initially released in 1988, Wolfram Mathematica systematically expanded into different fields, covering all types of technical computing and beyond. Today, it includes built-in libraries for analyzing and visualizing text documents, both structurally and semantically.
Its latest version improves the text, string, and natural language processing framework, providing more sophisticated tools for text analysis and symbolic manipulation. These tools can efficiently extract key values from unstructured data, identify word frequency data, and process natural language inputs.
More specifically, you can classify strings based on training data, find the closest matching string, identify text language, generate a word cloud from word frequencies or weights, and parse text into its grammatical structure.
- Programmatic segmentation of texts
- Normalization functions preprocess stopwords, capitalization, diacritics, and more
- Identifies grammatical structure and displays it as a graph and tree
And since the platform features an application programming interface, you can easily integrate it with third-party tools. It is available for all modern desktop systems, as well as in the cloud, through web browsers. Support is extended via documentation, phone, and live chat.
As for pricing, students can buy the desktop version for $181 or the cloud version for $11 a month. Small businesses, on the other hand, have to pay $3,230 or $194 a month for the same platform.
Released in 2003
Price: $999 per month for cloud analytics | Free demo is available
Rating: 8.5/10 from 100+ customers
Lexalytics has developed advanced text and sentiment analysis tools to transform complex documents into actionable insights. Its core technology is built on a multi-lingual text analysis engine named Salience.
The engine can be deployed on-premise and within a hybrid cloud infrastructure. No matter how large and complicated your documents are, it can uncover context-rich patterns and key metrics to improve business products and customer experience.
You can easily configure the platform to obtain out-of-the-box results. Make new queries, create custom entities, define category taxonomies, and add blacklists through an intuitive graphical user interface.
- Organize documents into customizable groups
- Determine sentiments and intentions of conversations and reviews
- Deploy custom-trained machine learning models to tackle specific problems
- Compatible with 30 languages and dialects
Overall, the platform can store, manage, and analyze unstructured documents in one place. You can easily set up data from various sources, assign roles and permissions to each user, monitor trends over time, and analyze performance across different fields.
Use the dashboard to compare trends, visualize results, and share findings with your colleagues.
However, unlike other text mining and analytics platforms, Lexalytics is quite expensive. Since both on-premise and cloud solutions cost almost $1,000 a month, it’s not meant for small teams.
Released in 2020
Price: Starts at $49 per month | 30-day free trial available
Rating: 8.3/10 from 300+ customers
Levity is a no-code SaaS platform that allows you to implement machine learning solutions in your company. From language detection to sentiment analysis, it’s an ideal AI platform for utilizing text mining.
Just integrate the data sources into Levity, and it will take care of the rest. It can analyze your emails, customer reviews, tickets, social media content, survey forms, and other written sources.
More specifically, Levity uses machine learning models to tag messages or emails based on their content. For instance, it can group messages with the following tags: personal, classified, or urgent. It can accurately detect sentiments in reviews and social media text (neutral, positive, or negative).
You can even build custom models to identify reviews or emails based on department, product, or any other entity specific to your company. Apply ‘urgency’ filters to quickly de-escalate negative reviews and brand mentions. This way, you can track customer responses to specific changes and prioritize bad reviews written by angry customers.
- Build custom AI blocks with a few clicks
- Train and test your custom AI without coding
- Define actions based on AI predictions
- Chain multiple blocks together
Levity connects with all popular tools, including Dropbox, Google Sheets, Gmail, OneDrive, Zapier, Zendesk, Slack, Hubspot, and ChatGPT. It also has an API, just in case.
As for pricing, the Startup plan costs $49 per month. It allows you to create unlimited AI blocks and perform up to 500 actions per month. The Business plan, on the other hand, lets you perform 5,000 actions for $139 a month.
2. Amazon Comprehend
Released in 2017
Price: Based on the size of requests | $0.0001 per unit | 1 unit = 100 characters
Rating: 8.1/10 from 100+ customers
Amazon Comprehend uses machine learning to find valuable insights from documents, emails, social media feeds, customer support tickets, and more.
Designed for businesses of all sizes, the platform simplifies document processing and reviewing workflows. It can efficiently extract topics, key phrases, sentiment, and intention from complex, large documents such as insurance claims and legal guardianship certificates.
You can even train machine learning models to classify content and detect specific terms. The best part is you don’t need any programming skills to do that.
Additionally, the platform can discover Personally Identifiable Information (PII) entities from documents and generate redacted versions of those documents. Redacting PII entities and sensitive data helps you protect users’ privacy and comply with laws and guidelines.
- Detects text written in 100+ languages
- Provides more granular sentiment insights
- Finds relevant topics or terms from various text documents
- Identifies and redacts personally identifiable information
Since the platform is fully managed by Amazon, you don’t need private servers to store and process documents. Just use the pre-trained APIs in your app and provide the source documents; the platform will take care of the rest. It will output key events, entities, topics, sentiment, and language in a JSON format.
You can start with a free tier that covers 5 million characters per API per month. It allows you to perform almost all crucial actions, such as extracting key phrases, recognizing entities, and analyzing syntax and sentiments.
1. Natural Language AI by Google Cloud
Released in 2018
Price: Free for up to 5000 units | $1 for 5,000-1,000,000 units | 1 unit = 1000 characters
Rating: 8.6/10 from 150+ customers
Google Cloud Natural Language allows you to utilize machine learning algorithms (pre-trained by Google) to carry out a variety of NLP tasks, such as entity extraction, content classification, and sentiment analysis.
Businesses can use Google’s powerful deep learning technologies to answer specific user queries and make the most of unstructured language text documents. Businesses can also develop their own AI apps without having to deal with the storage and management of their own training data sets.
- Classifies documents in 700+ pre-defined categories
- Syntax and entity analysis
- Sentiment analysis
- Multiple language support, including Chinese, Japanese, and Spanish
The platform is perfect for analyzing text documents (like email, chat, and product reviews), combining sentiment analysis and entity analysis, classifying content into pre-defined categories, and understanding the overall feeling or sentiment expressed in sentences.
While the Natural Language API uncovers the structure and meaning of text documents with thousands of pre-trained classifications, AutoML groups documents into custom categories as per your business requirements. Moreover, AutoML supports large data sets for complex use cases, including 5,000 classification labels, 1,000,000 documents, and a 10 MB document size.
Their pricing structure is quite unique — it follows a ‘pay-for-what-you-use’ model. Prices are calculated monthly based on which feature you have used and how many units you have used for those features. If your document contains 1,000 characters, it would be counted as one unit.
You won’t have to pay anything as long as the total number of units analyzed during the billing month remains below 5,000. Plus, all new users get $300 worth of free credits.
Other Equally Good Text Mining Tools
Released in 2017
Price: Starts at $2000 per month
Rating: 8.8/10 from 50+ customers
Thematic looks at all your feedback and helps you understand what customers say and why they return your products or leave services. It is like PowerBI or Tableau, but for text feedback.
You can link unstructured feedback from reviews, social media, live chat, support, surveys, and complaints with one click. Once integrated, Thematic can analyze all textual data and detect specific issues mentioned by customers.
It uses an AI-powered analysis engine to capture the meaning in individual sentences and categorize similar sentences, even if they are phrased uniquely.
- Categories thousands of reviews and feedback in minutes
- Quality-controlled and traceable
- Automatically discovers new issues
- Waterfall chart explaining trends
The platform also finds the root cause of changes in reviews, scores, or sentiment. It then shows critical metrics and specific pain points, giving you the complete picture. Filter down results by score, tenure, region, call length, and other variables you’ve included.
Overall, Thematic is designed to keep you ahead of competitors and improve decision-making with textual feedback data. You can start with a free trial plan, which allows you to upload up to three documents and see partial results.
Released in 2009
Rating: 8.5/10 from 20+ customers
Gensim is an open-source Python library designed to handle large text documents. Unlike other tools that target only in-memory processing, Gensim can process massive, web-scale corpora using data streaming and incremental online algorithm — it doesn’t require training corpus to reside fully in RAM at any given time.
It consists of unsupervised algorithms like Latent Semantic Indexing, Latent Dirichlet Allocation, FastText, and Word2Vec. These algorithms examine statistical co-occurrence patterns within a corpus of text documents to discover the semantic structure of documents.
- Platform independent
- Identifies semantically related documents
- Includes thousands of ready-to-use models for specific domains like health and legal
- Offers API support for seamless integration with other programming languages
In simple terms, Gensim uses modern statistical machine learning models to perform a range of natural language processing tasks, from document indexing and topic modeling to pattern recognition and information retrieval.
The library is used by thousands of organizations, including the National Institutes of Health, Search Metrics, 12K Research, Stillwater Supercomputing, and Cisco Security. And with more than 2,650 academic citations and 1 million downloads per week, it is one the most mature machine learning libraries.
Released in 1998
Price: $2,900 per year | 14-day free trial available
Rating: 8.2/10 from 40+ customers
WordStat is an easy-to-use software tool for extracting and analyzing information from large, unstructured documents. With one click, you can extract the most frequent sentences and most valuable topics in thousands of documents.
It can process about 25 million words per minute to extract valuable content quickly. It uses efficient techniques (like clustering, proximity plots, and multidimensional scaling) to identify patterns.
Additionally, it can leverage the power of multicore processing to process thousands of documents and instantly provide accurate results. For example, it can analyze a dataset containing 50,000 reviews and extract 5,000 most frequent phrases in just 0.4 seconds.
- Import data from numerous sources like web survey platforms and reference management tools
- Return to the source document with one click
- Get a quick summary of the most salient topics
- create your own categorization model with sentences or proximity rules
- Supports more than 70 languages
You can even integrate the software with third-party qualitative coding tools for a more in-depth analysis of specific documents. Associate textual information with geographic data and generate interactive plots of data points, heat maps, and thematic maps.
Once you get the desired result, export it into multiple formats like Excel, XML, Word, PNG, JPEG, and more.
So far, WordStat has been used in various applications, from analysis of new coverage and open-ended responses to fraud detection and business intelligence.
Released in 2001
Rating: 8.6/10 from 40+ customers
Carrot offers multiple text clustering and visualization tools. Carrot2, for example, is an open-source library to organize search results into thematic folders. It automatically identifies groups of related text and labels them with short keywords or phrases. The other (paid) tools are
- Lingo 3G: Instantly organizes thousands of text documents into clearly-labeled hierarchical folders. One can increase the quality of clustering by adding synonym definitions like images = pics = pictures = photographs.
- Lingo 4G: Enables interactive exploration of gigabytes of text and millions of documents. It extracts crucial topics, along with lexical relationships between them, within seconds.
You can try all of them for free for two months. Visualization tools are free forever, but they display a small logo on final outputs.
More to Know
What are the common algorithms used for text mining?
While thousands of algorithms are available to uncover hidden patterns, trends, and relationships in various types of content, the most popular ones are :
- Naive Bayes Classifier: It’s a probabilistic classifier used in high-dimensional training datasets
- K-Nearest Neighbor: Stores and classifies new data points based on similarity
- Support Vector Machines: Used for classification as well as regression problems
- Recursive partitioning decision trees: Splits data points based on dichotomous independent variables.
- K-Means Clustering: Divides unlabeled entities into clusters
Major applications of text mining?
Text mining tools have impacted the way numerous industries work, allowing them to deliver better customer experience and make data-driven business decisions. These tools have been applied to research, government, and business needs. Their most common applications include
- Risk management and cybercrime prevention
- Streamlined claim investigation
- Information retrieval from scientific literature
- Social media data analytics
- Spam filtering
- Contextual advertising
- Business Intelligence
Market size of text analytics platforms
The global text analytics market size is expected to exceed $29 billion by 2030, growing at a CAGR of 17.8% from 2023 to 2030.
The major factors behind this growth include the rising demand for predictive and social media analytics for businesses. Additionally, industry-specific applications and the need for highly customized services are positively impacting the market growth.
The United States is likely to remain a leader in this market due to the widespread adoption of text analytics solutions across end-user industries, including retail, technology, healthcare, and BFSI.
Why you can trust us?
We thoroughly analyzed 30+ text mining software and read customers’ reviews. It took about 24 hours to do the comprehensive research. Finally, we decided to shortlist the 13 software based on their AI capability, features, and ability to integrate with third-party tools.
Our “Rating” is the average of all ratings given by genuine customers on trusted review sites. In order to show you an accurate picture, we haven’t considered reviews and testimonials featured on the software’s official website.
We DO NOT earn commission from any of the featured tools. Furthermore, we have two independent editors who have no influence over our listing criteria or recommendations.
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