Sentimental Analysis of Movie Reviews Using NLP Techniques

The goal of natural language processing is to build machines that can comprehend text or speech input and respond to it in a way that is similar to how people do. To accomplish this, various Natural Language Processing (NLP) tasks are employed to analyze human language data, aiding computers in understanding the information it consumes. These tasks include sentiment analysis, voice recognition, part-of-speech tagging, and natural language generation. Sentiment analysis that detects emotions such as happiness, frustration, rage, and sadness goes beyond polarity. To do this, advanced machine learning techniques or lexicon collections of words and the emotions arouse are used. In this study, the Internet Movie Database (IMDB) Reviews Dataset is subjected to sentiment analysis to determine if a user's review is favourable or unfavourable based on the supplied details. Utilizing data from the real world, the proposed approach's correctness is assessed.