Analysing the Sentiments using Natural Language Processing

Abstract

Abstract: Sentiment analysis is an approach to identify, extract, quantify, and study affective states and subjective data that makes use of text analysis, computational linguistics, biometrics, and natural language processing. Customer materials, online and social media content, and medical materials all commonly use. Deep language models have made it possible to analyze more difficult data domains, such as news texts. The fundamental task is to categorize a text based on its polarity, that is, whether the expressed opinion is positive, negative, or neutral. In advanced sentiment classification, emotions like surprise, fear, anger, disgust, sadness, and pleasure are all looked at. Psychological studies and the General Inquirer are two sources of sentiment analysis's antecedents. Challenges in sentiment analysis include the possibility of opinion words being positive in one state being negative in another, and the fact that people may express opinions differently. Developing algorithms to identify and classify opinion or sentiment in electronic text is a goal.