Portal de Periódicos da CAPES

Machine Learning based Sentiment Analysis Using Django

2022; Medknow; Volume: 13; Issue: 4 Linguagem: Inglês

10.47750/pnr.2022.13.04.047

2229-7723

R. Yamini,

Advanced Computing and Algorithms

Before purchasing a product or service, People typically analyse the information about the product such as cost, warranty, quality etc.Only after getting satisfaction about such things, People try to buy that product based on the quality of service received.Since this process takes time and a chance of being duped by the dealer are higher, Sentiment analysis (SA) is necessary to purchase a product without any hesitation.Sentiment analysis examines reviews and comments of the products, which are in the form of text that requires several processes for providing the desirable information to the People.Moreover, SA is a significant research direction of Natural Language Processing (NLP).In this paper, a novel sentiment analysis model is developed based on the Machine Learning (ML) Algorithm, which provides an accurate sentiment information for the texts having different perspectives.The method of Stop words are used for data pre-processing.By using count vectorizer, the text data is converted into the form of vectors for extracting the desired features.Finally, the type of sentiment whether it is positive, negative or neutral is determined based on the ML classifier namely, Naive Bayes classifier.This model is developed using Django web framework that provides an accurate sentiment classification to the people or the industries who need the sentiment analysis.