10 Best Python Libraries for Sentiment Analysis (2022)

Sentiment analysis is a powerful technique that you can use to analyze customer feedback or monitor social media, for example. That said, sentiment analysis is very complicated, as it involves: unstructured data and language variations.

A natural language processing (NLP) technique, sentiment analysis can be used to determine whether data is positive, negative or neutral. In addition to focusing on the polarity of a text, it can also detect specific feelings and emotions, such as angry, happy and sad. Sentiment analysis is even used to determine intentions, such as whether someone is interested or not.

Sentiment analysis is a very powerful tool that is increasingly being used by all types of businesses, and there are several Python libraries that can help in carrying out this process.

Here are the 10 best Python libraries for sentiment analysis:

1. Cartridge

At the top of our list of the best Python libraries for sentiment analysis is Pattern, a multi-purpose Python library that covers NLP, data mining, network analysis, machine learningand visualization.

Pattern offers a wide range of functions, including finding superlatives and comparatives. It can also detect facts and opinions, making it stand out as a top choice for sentiment analysis. The function in Pattern returns the polarity and subjectivity of a given text, with a polarity result ranging from very positive to very negative.

Here are some of the key features of Pattern:

  • Multifunctional library
  • Find superlatives and equations
  • Returns polarity and subjectivity of given text
  • Polarity ranges from very positive to very negative


Another top option for sentiment analysis is VADER (Valence Aware Dictionary and Sentiment Reasoner), a rules/lexicon-based, open-source sentiment analyzer pre-built library within NLTK. Specifically designed for sentiments expressed in social media, the tool uses a combination of a sentiment lexicon and a list of lexical attributes that are generally labeled as positive or negative based on their semantic orientation.

VADER calculates the text sentiment and returns the probability that a particular input sentence is positive, negative, or neural. The tool can analyze data from all kinds of social media platforms, such as Twitter and Facebook.

Here are some of the key features of VADER:

  • Requires no training data
  • Understand the sentiment of text with emoticons, slangs, conjunctions, etc.
  • Excellent for texting on social media
  • Open source library


BERT (Bidirectional Encoder Representations from Transformers) is a top-of-the-line machine learning model used for NLP tasks, including sentiment analysis. Developed in 2018 by Google, the library was trained in English WIkipedia and BooksCorpus, and it turned out to be one of the most accurate libraries for NLP tasks.

Because BERT is trained on a large text corpus, it is better able to understand language and learn variability in data patterns.

Here are some of the key features of BERT:

  • Easy to tune
  • Wide range of NLP tasks, including sentiment analysis
  • Trained on a large corpus of unlabeled text
  • Deep two-way model

4. TextBlob

TextBlob is another great choice for sentiment analysis. The simple Python library supports complex analysis and operations on textual data. For lexicon-based approaches, TextBlob defines a sentiment by its semantic orientation and the intensity of each word in a sentence, which requires a predefined dictionary that classifies negative and positive words. The tool assigns individual scores to all words and a final sentiment is calculated.

TextBlob returns polarity and subjectivity of a sentence, with a polarity range from negative to positive. The library’s semantic labels aid in analysis, including emoticons, exclamation points, emojis, and more.

Here are some of TextBlob’s key features:

  • Simple Python Library
  • Supports complex analysis and operations on textual data
  • Assigns individual sentiment scores
  • Returns polarity and subjectivity of sentence

5. spaCy

SpaCy, an open-source NLP library, is another top option for sentiment analysis. The library enables developers to create applications that can process and understand massive amounts of text, and is used to build natural language understanding systems and information extraction systems.

spaCy allows you to conduct sentiment analysis to gather insightful information about your products or brand from a wide variety of sources, such as emails, social media, and product reviews.

Here are some of the key features of SpaCy:

  • Fast and easy to use
  • Great for budding developers
  • Processing large amounts of text
  • Sentiment analysis with a wide range of sources

6. CoreNLP

Stanford CoreNLP is another Python library that contains a variety of human language technology tools that help apply linguistic analysis to text. CoreNLP includes Stanford NLP tools, including sentiment analysis. It also supports a total of five languages: English, Arabic, German, Chinese, French and Spanish.

The sentiment tool includes several programs to support it, and the model can be used to analyze text by adding “sentiment” to the list of annotators. It also includes a command line for support and model training support.

Here are some of the key features of CoreNLP:

  • Includes Stanford NLP tools
  • Supports five languages
  • Analyzes text by adding “sentiment”
  • Command line of support and model training support

7. scikit learning

Scikit-learn, a standalone Python library on Github, was originally a third-party extension for the SciPy library. While it is especially useful for classic machine learning algorithms such as those used for spam detection and image recognition, scikit-learn can also be used for NLP tasks, including sentiment analysis.

The Python library can help you perform sentiment analysis to analyze opinions or feelings through data by training a model that can be executed if text is positive or negative. It offers several vectorizers to translate the input documents into vectors of attributes, and it comes with a number of different classifications already built in.

Here are some of the key features of scikit-learn:

  • Built on SciPy and NumPy
  • Proven with real-life applications
  • Diverse range of models and algorithms
  • Used by big companies like Spotify

8. Polyglot

Another great choice for sentiment analysis is Polyglot, an open-source Python library used to perform a wide variety of NLP operations. Based on Numpy, the library is incredibly fast and offers a wide variety of special commands.

One of the best selling points of Polyglot is that it supports extensive multilingual applications. According to the documentation, it supports sentiment analysis for 136 languages. It is known for its efficiency, speed and straightforwardness. Polyglot is often chosen for projects with languages ​​not supported by spaCy.

Here are some of the key features of Polyglot:

  • Multilingual with 136 languages ​​supported for sentiment analysis
  • Built on top of NumPy
  • open source
  • Efficient, fast and clear

9. PyTorch

Near the end of our list is PyTorch, another open-source Python library. Created by Facebook’s AI research team, the library allows you to run many different applications, including sentiment analysis, where it can detect whether a sentence is positive or negative.

PyTorch is extremely fast to run and can be used on simplified processors or CPUs and GPUs. You can extend the library with its powerful APIs and it has a natural language toolkit.

Here are some of the key features of PyTorch:

  • Cloud platform and ecosystem
  • Robust framework
  • Extremely fast
  • Can be used on simplified processors, CPUs or GPUs

10. Flair

Closing out our list of the 10 best Python libraries for sentiment analysis is Flair, a simple open-source NLP library. The framework is built directly on PyTorch and the research team behind Flair has released several pre-trained models for different tasks.

One of the pre-trained models is a sentiment analysis model trained on an IMDB dataset, and it is easy to load and make predictions. You can also train a classifier with Flair using your dataset. Although it is a useful pre-trained model, the data it is trained on cannot generalize as well as other domains, such as Twitter.

Here are some of Flair’s key features:

  • open source
  • Supports a number of languages
  • Easy in use
  • Several pre-trained models, including sentiment analysis

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