Python Libraries for Sentiment Analysis
- 01. NLTK’s TweetTokenizer - This tokenizer is designed for social media text, and it is capable of handling hashtags, mentions, and emojis.
- 02. TextBlob Python library is for processing textual data. Provides a simple API for diving into NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, etc.
- 03. SpaCy: a recognized choice for natural language processing in Python and includes built-in support for sentiment analysis.
- 04. Pattern library offers a variety of tools for data mining and machine learning, including support for vector space modeling and sentiment analysis.
- 05. Gensim is geared toward topic modeling and includes support for Latent Semantic Analysis, which can be used for sentiment analysis.
- 06. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool
- 07. PySentiment library provides functions for performing sentiment analysis on textual data.
- 08. Polyglot has polarity lexicons for 136 languages. The scale of the words’ polarity consisted of three degrees: +1 for positive words, and -1 for negatives words. Neutral words will have a score of 0.
- 09. Flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
- 10. BERT(Bidirectional Representation for Transformers) for Sentiment Classification
- 11. Building A GPT-3 Twitter Sentiment Analysis Product
- 12. Transformer XL is a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden-states to attend to longer context (memory)