10 Best Python Libraries for Sentiment Analysis (2022)

0

Sentiment analysis is a powerful technique you can use to do things like analyze customer feedback or monitor social media. That said, sentiment analysis is very complicated because it involves unstructured data and linguistic 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 anger, joy, and sadness. 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 deployed by all types of businesses, and there are several Python libraries that can help carry out this process.

Here are the 10 best Python libraries for sentiment analysis:

1. Pattern

Topping our list of the best Python libraries for sentiment analysis is Pattern, which is a versatile Python library that can handle NLP, data mining, network analysis, machine learning, and visualization. .

Pattern provides a wide range of features, including finding superlatives and comparisons. It can also perform fact and opinion detection, making it 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 Pattern’s main features:

  • Versatile library
  • Find superlatives and comparatives
  • Returns the polarity and subjectivity of the given text
  • Very positive to very negative polarity range

2. VADER

Another major option for sentiment analysis is VADER (Valence Aware Dictionary and sEntiment Reasoner), which is a pre-built open source rule/lexicon-based sentiment analyzer library in NLTK. The tool is specifically designed for sentiments expressed in social media, and it uses a combination of a sentiment lexicon and a list of lexical features that are generally labeled according to their semantic orientation as positive or negative.

VADER calculates the sentiment of the text and returns the probability that a given 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 main features of VADER:

  • Does not require training data
  • Understand the sentiment of text containing emoticons, slang, conjunctions, etc.
  • Excellent for social media text
  • Open source library

3.BERT

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

Because BERT was trained on a large corpus of text, it has a better ability to understand language and learn variability in data patterns.

Here are some of the main features of BERT:

  • Easy fine adjustment
  • Wide range of NLP tasks, including sentiment analysis
  • Trained on a large corpus of unlabeled text
  • Deeply bidirectional pattern

4. TextBlob

TextBlob is another great choice for sentiment analysis. The simple Python library supports complex parsing 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 classifying negative and positive words. The tool assigns individual scores to all the words, and a final sentiment is calculated.

TextBlob returns the polarity and subjectivity of a sentence, with a range of polarity from negative to positive. The semantic tags in the library facilitate parsing, including emoticons, exclamation points, emoticons, and more.

Here are some of the main features of TextBlob:

  • Simple Python library
  • Support for complex analysis and operations on textual data
  • Assign individual sentiment scores
  • Returns the polarity and subjectivity of the sentence

5. SpaCy

An open source NLP library, spaCy is another great option for sentiment analysis. The library enables developers to create applications capable of processing and understanding huge volumes of text, and it is used to build natural language understanding systems and information retrieval systems.

With spaCy, you can perform sentiment analysis to gather relevant information about your products or brand from a wide range of sources, such as email, social media, and product reviews.

Here are some of the main features of SpaCy:

  • Quick and easy to use
  • Ideal for beginner developers
  • Process huge volumes of text
  • Sentiment analysis with a wide range of sources

6. CoreNLP

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

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

Here are some of CoreNLP’s main features:

  • Integrates Stanford NLP tools
  • Supports five languages
  • Analyzes the text by adding “feeling”
  • Command line support and model training support

seven. scikit-learn

A standalone Python library on Github, scikit-learn was originally a third-party extension to the SciPy library. While particularly 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 sentiments through data by training a model that can output whether the text is positive or negative. It provides several vectorizers to translate input documents into feature vectors, and it comes with a number of different classifiers already built-in.

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

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

8. Polyglot

Another great choice for sentiment analysis is Polyglot, which is an open source Python library used to perform a wide range of NLP operations. The library is based on Numpy and is incredibly fast while offering a wide variety of dedicated commands.

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

Here are some of the main features of Polyglot:

  • Multilingual with 136 languages ​​supported for sentiment analysis
  • Built on NumPy
  • open-source
  • Efficient, fast and simple

9. TorchPy

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 perform many different applications, including sentiment analysis, where it can detect whether a phrase is positive or negative.

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

Here are some of the main features of PyTorch:

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

ten. Flair

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

One of the pre-trained models is a sentiment analysis model trained on an IMDB dataset, and it’s simple to load and make predictions. You can also train a classifier with Flair using your dataset. While this is a useful pre-trained model, the data it is trained on may not generalize as well as other areas, such as Twitter.

Here are some of Flair’s main features:

  • open-source
  • Supports a number of languages
  • Simple to use
  • Multiple pre-trained models including sentiment analysis
Share.

About Author

Comments are closed.