Sentiment Analysis: Exploring the Challenges, Processing, Tools,Approaches and Applications

Sentiment analysis is a branch of natural language processing (NLP) that uses computational tools to extract, identify and analyse subjective information from text. It has become a crucial tool in analysing massive amounts of data from online sources, such as social media, news articles, and customer reviews. The main approaches to sentiment analysis include rule-based analysis and machine learning-based analysis, with hybrid models becoming increasingly popular. Sentiment analysis has a wide range of practical applications, including monitoring brand reputation, predicting stock prices, and measuring public opinion. However, challenges exist in accurately interpreting the nuances of human language and evaluating performance. Advances in machine learning and NLP are continuously improving the accuracy and efficiency of sentiment analysis, leading to even deeper insights into the world of human language. In this article, we have examined various challenges, processing, dataset, tools, approaches and applications in the field of sentiment analysis.

Keywords:

sentiment analysis , Rule-based analysis , Machine learning , opinion , Deep learning