Businesses can analyze text data about their brand from emails, online reviews, social media conversations, and more with the aid of text mining software.

List of 8 best text mining software for 2023:

Levity – best all-rounder no-code solution
MeaningCloud – best for customizing
Apache OpenNLP – best for document categorization
Google Cloud NLP – best for syntax analysis
Textable – best free basic text analysis
SAS Text Miner – best for high-performance text mining
IBM Watson – best for multiple languages
DiscoverText – best for collaborative work

In addition to their text mining capabilities, these software programs were chosen for their full complement of machine learning features and capabilities. Let’s get started.

8 Best Text Mining Software

1. Levity – Best All-rounder No-code Solution

A no-code AI solution called Levity enables businesses to use machine learning in their routine business operations. All teams can use AI solutions thanks to simple AI workflows, without the need for an engineering or AI team.

When it comes to analyzing unstructured data, like images, text, or documents, other tools frequently fall short.

Levity’s top features include:

• With the AI block builder, you can quickly train AI models to classify data according to your preferred labels.
Utilize Levity’s intuitive labeling system to organize your data within the app.
• No-code AI workflows: create straightforward drag-and-drop AI workflows that define input and output so you can concentrate on other crucial business tasks. Use AI, for instance, to categorize emails instead of manually uploading them one at a time.
• Levity integrates with a number of important tools, including Zendesk, Gmail, and Slack, allowing you to easily gather data from a variety of sources. These integrations are useful when deciding on subsequent actions, such as notifying the support staff when a request for customer service is received.
• Add the human review to your AI models to reduce error. This is known as a “human-in-the-loop” review. By doing this, your model’s future accuracy is enhanced.

Now that we are aware of the essential features, it is time to weigh the advantages and disadvantages of the platform.

Here’s what Vetevo had to say about their experience using the platform for image classification:

“Although Levity’s method was very simple, we had never used machine learning in a lab before.”

With the features and capabilities required to implement Machine Learning solutions in your company, Levity is a strong Artificial Intelligence tool. Levity’s no-code AI solution is the perfect tool for utilizing text mining (also known as text analysis), which includes language detection and sentiment analysis.

2. MeaningCloud – Best for Customizing

Using text analysis software like MeaningCloud, businesses can implement text mining into their workflows. The platform provides numerous customization options for data analysis in addition to multilingual text analytics solutions in 57 languages, including Hindi, Arabic, Chinese, and others.

MeaningCloud’s key features include:

• Analyze incoming customer feedback using MeaningCloud, whether it comes from emails or sales calls.
• Social media monitoring is used to process huge amounts of data from various multilingual platforms.
• Utilize AI to transform your data into easily assimilated insights that you can use going forward.

The program is equipped with every essential feature a data mining tool should have, and it supports a large number of languages. Let’s examine the benefits and drawbacks.

3. Apache OpenNLP – Best for Document Categorization

Natural language processing is used by Apache OpenNLP, a machine learning-based toolkit, to assist businesses in their day-to-day operations. The tool is capable of carrying out typical NLP tasks like tokenization, named entity extraction, sentence segmentation, and more.

Apache OpenNLP’s top features include:
• Parser tools: to cut up text into smaller chunks based on semantic analysis and grammar rules.
• Identification of named entities, such as people, places, businesses, etc.
• Document classification: group documents according to their contents.

Businesses can use the software to carry out a wide range of NLP tasks for free. Let’s examine the advantages and disadvantages to see that it’s not all good.

Apache OpenNLP is an excellent free NLP tool, but it falls short when it comes to utilizing AI in your company. Users must first work with code, which makes it less user-friendly than other solutions.

4. Google Cloud NLP – Best for Syntax Analysis

Google’s natural language processing tool for companies is called Google Cloud NLP. It comes in three different forms: AutoML Natural Language for creating models, the Natural Language API for integrating into already existing applications, and Healthcare NLP API for examining medical text.

Google Cloud NLP’s top features include:
• Syntax analysis also referred to as parsing, is the process of analyzing natural language using grammar rules.
• Support for multiple languages: Use Google NLP in various languages.
• Sentiment analysis: find the underlying sentiment to interpret textual information.

As well as text classification and part-of-speech tagging (POS), the platform provides entity extraction, entity sentiment analysis, and entity analysis. There are many features, but what are the advantages and disadvantages?

Pricing is a bit hazy and different from any of the tools we’ve looked at so far. Companies are billed in accordance with the actions they perform—such as syntax analysis or sentiment analysis—and the number of units they do so on.

5. Textable – Best Free Basic Text Analysis

Businesses looking for text analysis can use the free, open-source software Textable. Teams can create AI models that process and analyze data visually thanks to the AI solution.

Textable’s top features include:
• Text analysis: Create a text analysis model that can divide the text into letters, words, sentences, or entire texts.
• Text recoding: quickly and easily change text before uploading it for analysis.
• Support and community forum: Textable has a knowledge base, but you can also chat with other users about problems in communities and forums.

Consider some of the advantages and disadvantages.

For businesses that don’t want to spend money on AI software but still need to perform basic text analysis tasks, Textable is fantastic. Since the platform is completely free to use, it is easily affordable; however, teams without a background in technology may find it challenging to use.

6. SAS Text Miner – Best for High-performance Text Mining

With the aid of SAS Text Miner, businesses can quickly gather and examine data from any text source, including comment sections, books, and other text sources, on the internet.

SAS Text Miner’s top features include:
• High-performance text mining: quickly analyze massive amounts of data using leading text mining techniques.
• Automatic Boolean rule generation: For simple content classification, the text mining software automatically generates true/false rules.
• Evaluation of the relevance of terms in a collection and comprehension of usage trends over time are two aspects of term profiling and trending.

The platform enables a visual results display and supports multiple languages. Your team can now explore relationships between terms and visualized results with ease.

Let’s examine the benefits and drawbacks.

SAS Text Mining is a capable text mining solution once users get the hang of it. There isn’t any pricing information available online, so interested parties must contact SAS directly to discuss plans.

7. IBM Watson – Best for Multiple Languages

IBM Watson is a collection of artificial intelligence tools made to lower adoption costs and barriers while maximizing results and responsible AI use. A number of major companies, including Caixa Bank, Lufthansa, and GSK, rely on this highly regarded text mining solution.

IBM Watson’s top features include:
• For better customer service, integrate a chatbox powered by Watson assistant into your website.
• By analyzing data, keeping an eye on trends, and managing surface patterns, Watson Discovery can help you unlock the value in your data.
• Using Watson’s visual recognition, images can be categorized and organized based on the objects they automatically recognize in the images.

The AI tools provided by IBM Watson are extensive and well-designed. The best AI solution for your company can be made by combining different solutions. Some of the advantages and disadvantages are listed below.

8. DiscoverText – Best for Collaborative Work

Text mining software called DiscoverText has features for multilingual text mining, data science, human annotation, and machine learning. Teams can sort unstructured text data quickly and easily using the platform’s point-and-click graphical user interface.

DiscoverText’s top features include:
• Custom sifters: to help make sure the information you gather is pertinent to what you’re trying to learn.
• Technical integration with Twitter: to make tweet extraction and analysis simple.
• Human input: By consistently exchanging information, humans, and machines can both grow and learn.

Small businesses and individuals can benefit from DiscoverText’s AI solutions. DiscoverText is excellent for getting started, especially if mining data from Twitter, but larger companies probably require a more sophisticated solution.

Let’s look at some of the advantages and disadvantages.

How to Choose the Right Text Mining Software? You Should Consider:

Scalability

can your chosen solution grow with your business and Do months of back-and-forth over AI upgrades even exist?

Ease of Use

can your team learn to use the software with little difficulty? How user-friendly and intuitive is the interface?

Capabilities

does the text mining solution do what you need it to? Do the predictions and outcomes produced by the AI software hold up?

Support

is there support on hand to help you navigate issues? Does the provider have a staff of people to help?

Summary

There are many text mining tools available that can unquestionably be useful for tasks like named entity extraction, sentiment analysis, keyword extraction, NPS analysis, and more.

Now you just have to figure out what’s best for you.

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