The Comparative study of Python Libraries for Natural Language Processing (NLP)

Abstract

Natural Language Processing (NLP) has seen significant advancements in recent years, driven largely by the availability of powerful Python libraries. This comparative study aims to analyze and compare the performance, language support, community support and ease of use of many popular Python libraries for NLP like NLTK (Natural Language Toolkit), spaCy, TextBlob, Flair, Jina, Gensim etc. The study evaluates these libraries across various NLP tasks such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text summarization. Additionally, the paper discusses the strengths and weaknesses of each library, providing insights into their suitability for different NLP applications. Through detailed experimentation and analysis, this study aims to guide researchers and practitioners in selecting the most appropriate library for their NLP projects.