Gensim

Gensim is a Python library specifically designed for topic modeling and document similarity computation. It implements classic models like Word2Vec, FastText, Doc2Vec, and LDA, enabling efficient training of word and document vectors. Gensim excels at handling large corpora with support for streaming and memory optimization. It is commonly used in search engines, recommendation systems, and text clustering applications.

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NLTK

NLTK is one of the most classic natural language processing toolkits in Python, designed primarily for teaching and research purposes. It offers a wide range of corpora, lexical resources, and text processing functions such as tokenization, part-of-speech tagging, parsing, and named entity recognition. NLTK's interface is clear and easy to understand, making it ideal for beginners learning NLP.

spaCy

spaCy is a fast and industrial-strength natural language processing library that focuses on efficient and accurate text analysis. It supports multiple languages and includes built-in features like named entity recognition, dependency parsing, and word vector representations. spaCy’s core modules are written in Cython, offering high performance suitable for large-scale data processing.

Transformers

Hugging Face Transformers is one of the most popular deep learning NLP libraries, integrating a vast collection of pre-trained models based on the Transformer architecture, including BERT, GPT, T5, and RoBERTa. Supporting both PyTorch and TensorFlow backends, it provides a unified API for model loading, fine-tuning, and deployment. Users can implement tasks like text classification, question answering, and machine translation with minimal code.

TextBlob

TextBlob is a simple and user-friendly NLP library that wraps some of NLTK's functionalities, targeting beginners and lightweight use cases. It provides common features like sentiment analysis, noun phrase extraction, spelling correction, and translation. TextBlob's API resembles Python's built-in types, offering a clean and intuitive syntax suitable for rapid prototyping or small projects.

Stanza

Stanza is the official Python wrapper for Stanford CoreNLP, providing high-quality multilingual NLP capabilities. It supports multiple languages, including Chinese, with features such as part-of-speech tagging, dependency parsing, named entity recognition, and sentiment analysis. Built on deep learning models, Stanza delivers high accuracy, making it suitable for academic and commercial applications requiring precise linguistic analysis.