Sentiment Analysis on Book Reviews Using Machine Learning Techniques

Abstract

Among the most essential NLP applications is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has received a lot of attention. It is concerned with text classification in order to establish the intent of the text's final user. End-user feedback becomes the most important factor in determining the quality of a book's content. Online book reviews are regarded as one of the most important sources of customer feedback. In the current environment, people may discover on books and make selections based on online review sites. Hence, extracting the precise book reviews from the dataset wherein we have preprocessed the dataset using sentimental analysis, counter vectorizer are used for feature extraction in bag of words and the classification are done through SVM, KNN, RF, DT, NB to predict the review status whether it is positive, negative or neutral. The model is tuned with grid search and the confusion matrix are plot for the above mentioned algorithms. The final aim of the work is to provide users with their wanted books. Thus, we have tested our categorization approach through amazon reviews.