A Comprehensive Review on Python based NLP Approaches for Sentiment Analysis
Keywords:
NLTK, Textblob, Sentiment analysis, Polarity, FlairAbstract
Sentiment analysis, also known as opinion mining, is a burgeoning field in natural language processing that involves the computational analysis of textual data to determine and quantify the sentiment expressed within. This study presents a comprehensive exploration of sentiment analysis using Python, focusing on techniques, methodologies, and tools that enable the extraction of sentiment polarity from diverse textual sources. The review commences with an overview of the importance of sentiment analysis in understanding public opinion, customer feedback, and social media dynamics. A significant portion of the study is dedicated to practical implementation using Python, showcasing popular libraries such as NLTK, TextBlob. It also covers portions such as summarization, challenges in sentiment analysis, and applications.
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