Text mining helps organizations get more from their data—whether that's customer reviews and survey responses, or sales call transcripts and email chains. A variety of methods—such as Natural Language Processing (NLP), data mining, and other text classifiers—enable companies to uncover actionable insights in text data that help them make better business decisions.
Manually sorting through data is time-consuming and inefficient, and it's also subject to human error and bias. Organizations need a better, quicker way to extract and analyze data—they need some pretty comprehensive text mining software.
Good thing we've got the top eight right here, right now.
These tools have been selected not only for their text mining capabilities but also for their comprehensive suite of Machine Learning features and capabilities. Let's get started.
1. Levity - best all-rounder no-code solution
Levity Workflow: Tag email attachments
Levity is a no-code AI solution helping organizations harness Machine Learning in their day-to-day business processes. The intuitive AI workflows enable all teams to use AI solutions—without the need for an engineering or AI team.
Other tools often fall short when it comes to analyzing unstructured data such as images, text, or documents.
Levity's top features include:
AI block builder: easily train AI models to categorize data into your chosen labels.
Integrated labeling system: organize your data in-app with Levity's easy-to-use labeling system.
No-code AI workflows: build simple drag-and-drop AI workflows that define input and output so you can focus on other important business tasks. For example, use AI to categorize emails without the need to manually upload emails one by one.
Ample integrations for data mining: Levity integrates with several key tools—such as Zendesk, Gmail, and Slack—to enable you to seamlessly gather data from a number of sources. These integrations also help when considering next steps—for example, notifying the support team when a customer support query comes in.
Human-in-the-loop review: minimize error by adding human review to your AI models. This helps improve the accuracy of your model moving forward.
Levity AI: Setting Up Human in the Loop
So, now we've got the key features —let's consider the pros and cons of the platform.
Pros
Intuitive interface simplifies AI for non-techy users
Build AI flows in a matter of minutes
Customize AI models as per your needs
Minimize error with human review
Cons
Currently in beta so not yet available to all businesses
Here's what Vetevo had to say about their experience using the platform for image classification:
"We had never used machine learning in a lab before but Levity's process was really straightforward."
Levity is a powerful Artificial Intelligence tool with the features and capabilities necessary to implement Machine Learning solutions in your organization. From sentiment analysis to language detection—Levity's no-code AI solution is an ideal tool for utilizing text mining (also referred to as text analysis).
Try it out yourself
Create your own AI for documents, images, or text to take daily, repetitive tasks off your shoulders.
MeaningCloud is a text analysis software that provides solutions to organizations looking to implement text mining in their operations. The platform offers multilingual text analytics solutions in 57 languages—including Hindi, Arabic, Chinese, and more—as well as ample customization options for analyzing data.
MeaningCloud’s key features include:
Voice of the customer: use MeaningCloud to analyze incoming customer feedback, whether that's emails or sales calls.
Social media monitoring: for processing large volumes of data from a variety of multilingual platforms.
Graphic interface: use AI to turn your data into digestible insights that you can use moving forward.
The software has all the key features you need from a data mining tool and can support a wide variety of languages. Let's take a look at the pros and cons.
Pros
Free demo and free plan for companies with little to no budget
Integrates with many other analysis tools
Multiple language packs are available
Cons
Not available as a standalone platform—only as an Excel add-in or cloud API
Can be expensive past free plans as additional features and languages require additional fees
3. Apache OpenNLP - best for document categorization
Apache Open NLP Interface. Source: https://opennlp.apache.org/
Apache OpenNLP is a machine learning-based toolkit that uses natural language processing to support businesses in their day-to-day. The tool is capable of performing common NLP tasks, such as tokenization, named entity extraction, sentence segmentation, and more.
Apache OpenNLP's top features include:
Parser tools: to separate text into smaller pieces based on grammar rules and semantic analysis.
Named entity recognition: identifies named entities such as people, places, companies, etc.
Document categorization: categorize documents based on their contents.
The software enables businesses to perform a wide variety of NLP tasks without paying a cent. However, it's not all good—let's take a look at the pros and cons.
Pros
A free, open-source tool
Ample NLP capabilities
Cons
The project is no longer active, meaning the software is no longer up to date
According to user reviews, some functionalities work better than others
More complex than no-code solutions so not accessible to all levels
Apache OpenNLP is a great tool for free NLP—but it's lacking when it comes to harnessing the power of AI in your business. It's not as user-friendly as other solutions and requires users to work with code to get started.
4. Google Cloud NLP - best for syntax analysis
Google Cloud NLP Interface. Source: https://cloud.google.com/natural-language
Google Cloud NLP is Google's natural language processing solution for businesses. It comes in three different varieties: AutoML Natural language for building models, the Natural Language API for adding to existing applications, and Healthcare NLP API for analyzing medical text.
Google Cloud NLP's top features include:
Syntax analysis: also known as parsing, is the process of analyzing natural language with the rules of grammar.
Multi-language support: use Google NLP in a number of languages.
Sentiment analysis: uncover the meaning behind textual data by looking for the underlying sentiment.
The platform also offers entity extraction, entity sentiment analysis, text classification, and part-of-speech tagging (POS). There are ample features, but what are the pros and cons?
Pros
Easily integrates with Google Cloud services
The NLP API is a powerful tool to use with existing applications
Cons
Can be a confusing solution for beginners
Can get very expensive as needs increase
Implementation can take some time
Pricing can be a little confusing and isn't similar to any of the tools that we’ve looked at so far. Companies are charged based on what action they're performing—such as syntax analysis or sentiment analysis—and how many units they're performing it on.
5. Textable - best free basic text analysis
Textable Interface. Source: http://textable.io/
Textable is a free, open-source software for businesses looking for text analysis. The AI solution enables teams to build AI models that process and analyze data visually.
Textable's top features include:
Text analysis: build a text analysis model that can segment letters, words, sentences, or full pieces of text.
Text recoding: alter text quickly and easily before uploading it for analysis.
Support and community forum: Textable has a knowledge base, but you can also discuss issues within communities and forums with other users.
Let's look at some of the pros and cons.
Pros
Can be used alongside other tools, such as Unicode for text encoding
Developed in academia with cutting-edge data science research
Pre-made model templates for common use cases
Cons
More complex than no-code solutions
No human support
Textable is great for organizations looking to perform basic text analysis functions without investing in AI software. The platform is entirely free to use, making it accessible financially—however, it can be complicated for teams with no tech background.
6. SAS Text Miner - best for high-performance text mining
Sas Text Miner Interface. Source: https://www.sas.com/en_gb/software/text-miner.html
SAS Text Miner enables organizations to easily collect and analyze data from all over the web—be it comment fields, books, or other text sources.
SAS Text Miner's top features include:
High-performance text mining: quickly evaluate large volumes of data using top text mining procedures.
Automatic Boolean rule generation: the text mining software automatically creates true/false rules for easy content classification.
Term profiling and trending: evaluate the relevance of terms in a collection and understand usage trends over time.
The platform supports multiple languages and allows for a visual display of results. This enables your team to easily analyze visual results and explore relationships between terms.
Let's take a look at the pros and cons.
Pros
Drag and drop labels
Integrates with other SAS applications
Cons
Requires training for use
The program takes time to study large amounts of data
Once users get the hang of it, SAS Text Mining is a strong text mining solution. Interested parties will need to get in touch with SAS directly to discuss plans, as there's no pricing information available online.
7. IBM Watson - best for multiple languages
IBM Watson Interface. Source: https://www.ibm.com/uk-en/products/watson-assistant/integrations
IBM Watson is a group of artificial intelligence tools designed to reduce the costs and hurdles of AI adoption while optimizing outcomes and responsible use of AI. It's a highly rated text mining solution used by key players in a variety of industries, such as Caixa Bank, Lufthansa, and GSK.
IBM Watson's top features include:
Watson assistant: add an AI-powered chatbox to your website for better customer service.
Watson discovery: unlock the value in your data by analyzing data, monitoring trends, and managing surface patterns.
Watson visual recognition: automatically identify objects in an image and organize and classify these images into logical categories.
The IBM Watson suite of AI tools is comprehensive and refined. Solutions can be mixed and matched to create the ideal AI solution for your business. Here are some of the pros and cons to consider.
Pros
Feature-rich
High-quality services
Can pick and choose AI features
Cons
Some difficulties reported when using the software in any language that isn't English
DiscoverText is a text mining solution that offers multilingual, text mining, data science, human annotation, and machine learning features. The platform's point-and-click graphical user interface enables teams to sort unstructured text data quickly and easily.
DiscoverText's top features include:
Custom sifters: to help ensure the data you collect is relevant to what you're looking to uncover.
Technical integration with Twitter: for easily extracting and analyzing tweets.
Human input: consistent back and forth between humans and machines enables both to learn and improve.
DiscoverText is a small business providing AI solutions to individuals and small businesses. Larger companies likely need a more developed solution, however, DiscoverText is great for getting started—especially if mining data from Twitter.
Let's take a look at some of the pros and cons.
Pros
Support videos from the founder
Easy to learn and use
Strong social media research tool
Cons
The underdeveloped interface appears clunky
Some features confusing at first
Choosing the right text mining software for your business is no small task—you need to evaluate your present and future needs. You should consider:
Scalability:can your chosen solution grow with your business and AI needs? Does upgrading require months of back and forth?
Ease of use:can your team learn to use the software with little difficulty? Is the interface intuitive and smooth?
Capabilities:does the text mining solution do what you need it to? Are the AI software's predictions and results accurate?
Support:is there support on hand to help you navigate issues? Does the provider offer a human support team?
Text mining tools can be useful across a wide variety of industries. Organizations both big and small typically use text mining tools to speed up processes and avoid repetitive tasks.
3. What are some text mining examples?
Some examples of text mining include:
Sentiment analysis
Entity recognition
Syntax analysis
Information retrieval
Clustering
Take a closer look at some of the use cases for text mining here or join the next Levity demo.
We'll run you through the ins and outs of the platform, and how you can use it to automate repetitive, mundane tasks.
Now that you're here
Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.If you liked this blog post, you'll love Levity.
Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.If you liked this blog post, you'll love Levity.
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