Best Text Mining Tools of 2021

Best Text Mining Tools of 2021

Text mining (also known as) text analysis is the automated process of transforming unstructured text into easy-to-understand and meaningful information. It can be used to extract entities and sort text by sentiment, topic, intent, urgency and more.

Equipped with Natural Language Processing (NLP), text mining tools are used to analyze all types of text, from survey responses and emails to tweets and product reviews, helping businesses gain insights and make data-based decisions.

The great news is there are plenty of online resources and tools that can help you get started with text mining. The conundrum that many businesses face, however, is whether to build or buy text mining software.

If you know how to code, you might want to use open-source libraries to build your own text mining models. However, if you don’t have the time or resources, we recommend SaaS tools. Not only are they cost-effective, but they’re also accurate, and reliable.

To top it all off, you can take advantage of these text mining tools almost instantly.

Top 8 Text Mining Tools

1. MonkeyLearn

Best for: Small, medium and large businesses that want to extract valuable information and turn it into actionable insights.

User-friendly in every possible way and equipped with powerful machine learning algorithms, MonkeyLearn is a text analysis platform that includes a suite of text classifiers and extractors for multiple text mining purposes.

You can choose from pre-trained models for sentiment analysis, topic classfication, keyword extraction, and more, and run these analyses in unison in MonkeyLearn Studio.

This all-in-one text analysis and data visualization suite makes it really easy to analyze data and gain insights. s, Choose from custom designed Workflows for survey data analysis, social media monitoring, and customer support tickets analysis, and visualize your data in a striking dashboard.

While Workflows are equipped with pre-trained machine learning models, you can easily create your own based on your unique needs and industry-specific texts, which yields the most accurate results.

MonkeyLearn also has multiple integrations with everyday apps such as Excel, Google Sheets, Zapier, and Zendesk, to name a few, making it easy to use with the software you already work with. Alternatively, you could use MonkeyLearn’s API available in all major programming languages.

Discover plans and pricing or request a demo to learn more.

2. Aylien

Best for: Developers who want to collect, analyze, and understand human-generated content at scale. 

Aylien is a cloud-based tool that makes use of artificial intelligence, natural language processing, and machine learning, to collect, analyze, and understand human-generated content. From labeling documents, tracking issues, and performing sentiment analysis, Aylien extracts meaning from text and helps its clients make data-driven decisions.

Aylien’s easy-to-use text analysis APIs, and its range of text mining models (document categorization, sentiment analysis, entity extraction, content aggregation, topic discovery, and more) make it a favorite amongst developers and data scientists.

Aylien’s most popular tool is the News API, which searches, sources, and analyzes news content in real-time. 

3. IBM Watson

Best for: SMBs and large companies that want advanced text analytics for content taxonomy.

IBM Watson is an AI platform that helps you unlock value from data. Among its array of tools, are the Watson Natural Language Classifier, Watson Personality Insights, and the Watson Tone Analyzer.

Regardless of where customers are on their AI journey, the Watson Natural Language Classifier lets them build custom machine learning models to analyze and label texts. Users can upload training data in .csv format then use the classifier to categorize texts, extract insights, and identify trends.

Watson Personality Insights lets customers take advantage of linguistic analytics to predict personality traits, habits, and preferences found in written texts. It enables a deeper understanding of customer habits and preferences, all based on customer interactions from emails, tweets, and any online posts.

Finally, the Watson Tone Analyzer examines emotions and tones in customer feedback, such as tweets, surveys, or reviews, allowing brands to monitor customer sentiments.

4. Thematic

Best for: Medium to large-sized companies who receive large volumes of customer feedback.

Thematic is an AI-powered end-to-end solution that focuses on helping companies transform customer feedback into actionable insights.

Products include Thematic Intelligence, Thematic Insights, and Thematic Catalyst. Thematic Intelligence pinpoints the meaning behind texts and groups similar phrases into themes, then employs sentiment analysis to classify text data as negative or positive.

Thematic Insights alerts you to key trends and fluctuations by highlighting relationships and patterns in customer feedback. You can gain even deeper insights by breaking down results into sub-themes and customer groups, and incorporate metrics to identify drivers, root causes, and solutions.

Last, but not least, Thematic Catalyst helps create dashboards with key insights, so you can convince partners and stakeholders to implement change. It focuses on giving customer support the tools that they need to help them take immediate action on insights drawn from customer feedback. 

5. Google Cloud NLP

Best for: Medium to large-sized companies that are looking for a pay-for-what-you-use service, for model building and predictive analytics. 

Google Cloud NLP gleans insights from unstructured text through the use of machine learning. It helps extract meaningful information about people, places, and events, and enables you to analyze text, as well as integrate it with your document storage on Cloud Storage for a seamless experience.

Through its AutoML Natural Language and Natural Language API resources, users can create custom machine learning models to classify content and employ different text mining techniques, such as sentiment analysis, entity extraction, topic classification, and more. 

6. Amazon Comprehend

Best for: Companies that require a low-learning curve product that enables high-level analysis of customer text data. 

Amazon Comprehend is a Natural Language Processing service that employs machine learning to unearth insights and find correlations in text. It’s capable of extracting phrases, places, people, brands, and events, as well as understanding the sentiment behind text and automatically organizing data by topic.

 

This tool can yield insightful research and analytics that power data-driven decisions. And as a fully managed solution, it’s not necessary for clients to go through the motions of creating, training, and maintaining data. 

With that being said, clients can improve or customize their models, with little or no expertise in machine learning, by simply feeding the model with more sample texts. In addition, the platform offers AutoML (automatic machine learning) capabilities, so customers can create fully customized models that are unique to their needs.

7. MeaningCloud

Best for: For developers of SMBs and large companies that want to extract meaning from unstructured content at an affordable price.

Focused on text mining and semantic analysis, MeaningCloud hosts a set of APIs that provide different text analytics functionalities, including sentiment analysis, text classification, topic extraction, deep categorization, and more.

MeaningCloud is perfect for developers, as you can use text analysis APIs with just a few lines of code, allowing businesses to analyze unstructured text from any channel of communication (email, chatbots, surveys, social media) and better manage customer experiences.

8. Lexalytics

Best for: Medium to large-sized companies who process high volumes of data and require on-premise security or their own private cloud.

Lexalytics offers three main tools to analyze text: Salience, Semantria, and SSV (Storage & Visualization).

 

Salience is the on-premise solution that offers companies full access to Natural Language Processing and text analytics libraries from their own servers. Models include sentiment analysis, categorization, entity recognition, theme analysis, intention detection, and summarization.

If you need more flexibility, Semantria offers text and sentiment analysis APIs in the cloud, and with SDKs in all major programming languages.

SSV connects with client data to generate customizable visualization dashboards, or exports to a preferred intelligence tool to unearth trends or patterns in data.

Final Words on Text Mining Tools

Customer feedback and online interactions are a constant source of information for businesses. The problem is that this data needs to be analyzed, to gain insights and improve customer experiences.

You could rely solely on humans to analyze this data manually, but it’s extremely time-consuming and tedious. The solution is text mining software, powered by Natural Language Processing (NLP) and machine learning, which automatically transforms text data into information that computers can understand.

Instead of building your own text mining tools, it’s more cost-efficient to use one of the many SaaS tools available online, each offering unique text mining models, from sentiment analysis to keyword extraction.

MonkeyLearn should be top of your list for its easy-to-use controls, intuitive interface, and no-frills setup. Explore MonkeyLearn Studio, and see for yourself how easy it is to analyze and visualize your data all in one place your texts.

Or request a demo to learn more about how to use MonkeyLearn’s text mining tools.**

Rachel Wolff

March 4th, 2020

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