NLP Models for Sentiment Analysis
- 01. NLTK’s TweetTokenizer: this tokenizer is explicitly designed for social media text, and it is capable of handling hashtags, mentions, and emojis.
- 02. TextBlob is a Python (2 and 3) library provides a simple API for diving into NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification and translation
- 03. spaCy excels at large-scale information extraction tasks
- 04. Pattern - Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
- 05. Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities
- 06. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.
- 07. pysentiment - This is a library for sentiment analysis in dictionary framework
- 08. Polyglot is a natural language pipeline that supports massive multilingual applications.
- 09. Flair allows you to apply our state-of-the-art NLP models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data,
- 10. Bidirectional Encoder Representations from Transformers (BERT) is a Machine Learning (ML) model for natural language processing
- 11. Generative pre-training model (GPT) by OpenAI - to perform both unsupervised learning and supervised learning to learn text representation for NLP downstream tasks.
- 12. Transformer-XL is a pre-trained language model that can be fine-tuned for a variety of tasks, including sentiment analysis.
- 13. XLNet - a general-purpose autoregressive pre-trained language model.
- 14. RoBERTa - A rotation-invariant version of BERT.
- 15. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base.