16 Best Sentiment Analysis Tools and Services for Machine Learning

Article by Lucas Scott | August 19, 2020

Sentiment analysis tools and resources are quickly becoming one of the best ways to understand your customers and their thoughts about your brand or product. By compiling, categorizing, and analyzing user opinions, businesses can prepare themselves to release better products, discover new markets, and most importantly, keep customers satisfied.

The Internet is full of tools and services to help you create or refine your sentiment analysis system, but knowing what best fits your needs can be difficult to determine. To help, we’ve put together a list of some of the best tools, resources, and services for sentiment analysis. This includes the dashboards that do it for you and the open-source tools that help you do it yourself.


Dashboards and Data Analysis Platforms

The following data analysis platforms and dashboards all offer sentiment analysis as part of their services. The key to finding the right tool here starts with defining your needs (social media, call analysis, customer feedback, etc.)

Awario: This social media monitoring tool allows you to track and analyze specific keywords across the web. It can help you with sentiment analysis in a variety of languages, and their dashboard makes it simple to categorize data by country and importance.

Brandwatch: As the name suggests, Brandwatch puts its focus on data analysis to protect, analyze, and improve your brand. Their sentiment analysis tools can help you understand how people talk about both you and your competitors. Their platform also allows you to work with both text and image data.

Talkwalker: Talkwalker boasts sentiment analysis services across 187 languages. You can discover trending stories in real time, track your top influencers, and pull data from television and radio.

Clarabridge: The focus of Clarabridge is managing and analyzing customer feedback. The platform can capture and categorize reviews, surveys, and calls. You can analyze these for points of friction to help you improve the customer experience.

Repustate: The Repustate API can be integrated easily thanks to their support with a variety of popular client libraries. Their sentiment analysis and semantic insight extraction is available in 24 languages and can be used for news, blogs, forums, social media, and in-house company data.

Rapidminer: Rapidminder offers sentiment analysis as part of their data science platform, through which you can conduct analysis on both text and audio data. The service can also help you improve fraud detection.

Rosette: The Rosette platform covers sentiment analysis along with a host of other text analytics including entity extraction and chat translation, as well as topic and relationship extraction.

Lexalytics: The Lexalytics text analysis platform is recommended for companies processing high volumes of data. Along with the analysis platform, they also offer a data management platform capable of visualizing your data for easier understanding.


Sentiment Analysis Tools and Services to Develop a Custom Data Solution

The best sentiment analysis tools understand the unique language your customers speak. This includes everything from their geographic location and their dialect to their culture, colloquialisms, and slangs. This will increase the accuracy of your sentiment analysis projects and give you better data to work with.

Lionbridge AI: Lionbridge’s data annotation software allows for easy sentiment classification along with access to NER tagging, text classification, and audio transcription. If you don’t have your own team of annotators, Lionbridge can provide a trained team from their community. Project management, additional annotators, and 24/7 support is available as your project grows in scope.

lionbridge sentiment analysis tools for annotation


Scale AI: Natural language processing is a part of Scale’s data services, which includes data classification, machine translation, and sentiment analysis. Their work focuses on the collection and annotation of text data for building machine learning systems.

MonkeyLearn: Monkey Learn offers pre-trained sentiment analysis models ready for immediate use that can be easily integrated with a variety of apps. They can also help you build a customized sentiment analysis model trained on your own in-house data.

IBM Watson: The Watson Tone Analyzer is part of IBM’s cloud services, and can be used to analyze tweets and reviews, monitor customer support conversations, and help chatbots to detect a user’s tone and respond accordingly.


Open Source Sentiment Analysis Tools

The following open source tools are all free and available for building and maintaining your own sentiment analysis infrastructures and other NLP systems. However, do keep in mind that in order to make use of the tools below, you or someone on your team will need the necessary programming and development skills to handle the coding and ML integration.

NLTK: The Natural Language Toolkit is a platform for building Python programs to work with human language data. This includes lexical analysis, named entity recognition, tokenization, PoS tagging, and sentiment analysis. It also offers some great starter resources.

Spark NLP: Considered by many as one of the most widely used NLP libraries, Spark NLP is 100% open source, scalable, and includes full support for Python, Scala, and Java. You’ll find a whole host of NLP features, pre-trained models and pipelines in multiple languages. There’s also an active Slack community for discussion and troubleshooting.

TextBlob: Built on the shoulders of NLTK, TextBlob is like an extension that simplifies many of NLTK’s functions. It offers an easy to understand interface for tasks including sentiment analysis, PoS tagging, and noun phrase extraction. TextBlob is a recommended natural language processing tool for beginners.

Doccano: This open source text annotation tool has been designed specifically for text annotation. It allows for the creation of labeled data for sentiment analysis, named entity recognition, and text summarization. This is a good option to look at for smaller datasets and building initial proof of concept projects.


Sentiment Analysis Datasets

If you’re looking for datasets to start or supplement your sentiment analysis systems, be sure to check our dedicated collection of free sentiment analysis datasets. It includes text data for social media, product reviews, and brand management.

If you’re still not sure exactly what your sentiment analysis data needs are, get in touch. Lionbridge provides custom datasets for sentiment analysis in over 30 languages. We can help you define your project needs, and help you build the data foundations necessary for your high-quality sentiment analysis system.

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The Author
Lucas Scott

Lucas is a seasoned writer, with a specialization in pop culture and tech. He spends most of his free time coaching high-school basketball, watching Netflix, and working on the next great American novel.


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