Sentiment analysis, opinion mining and topic modelling of epics and novels using machine learning techniques

Abstract

Sentiment analysis systems can collect and automatically structure unstructured information by collecting public views of services, products, policy, brands, etc. This information is of major value in the fields of marketing analysis, public relations, product reviews, net promoter evaluations, customer feedback and client reviews. Literary works, on the other hand, are less susceptible to computational analysis because there are no immediate commercial incentives. However, similar techniques can be used to evaluate literary work, comprehend the underlying social network, and obtain or validate literary work. This project is about analyzing the book's characters and predicting their characteristics and relationships with one another. A lot of human effort is expended during the adaptation of a novel/book in any form, which is inconvenient and undesirable. Furthermore, the human brain has a tendency to overlook a number of minor details about the events/characters in the book. The scenario described above can frequently result in inaccuracies in the adaptation's plot. As a result, the project is an innovation that aims to aid in the easy and accurate adaptation of a book, making the process much simpler and precise. Machine learning (supervised and unattended) and lexical approaches include the current sentiment analysis techniques. The model's goal is to scan the massive amounts of text in the book. Following digitization, the model will display interesting ideas derived from the given book using a combination of natural language processing, feelings and emotions analysis, and social network analysis methodology.