Sentiment Analysis using SVM, Naïve Bayes, and LSTM
Parul Jain, Ritu Agarwal · 2023
Many websites such as Twitter, blogs, and ecommerce sites are popular nowadays, which display a tremendous amount of information about various topics, such as reviews and discussions on events. To manually try to understand the essential opinion regarding something is very time-consuming. Opinion mining or sentiment analysis is used, which automatically analyzes text using machine learning approaches and tries to give the idea of people’s sentiments regarding a topic or product. Nykaa is one of the leading online shopping websites, where a large amount of information is available. The paper uses sentiment analysis on the Nykaa dataset, where we train the machine to generate the ability to define the overall opinion about a particular context, such as negative or positive. The input data undergoes pre-processing before being transformed into a vector space, as machines only understand numbers, not text, using sentiment scores. Then machine learning algorithms like SVM, Naïve Bayes, and LSTM are applied, and results are evaluated and compared where LSTM performs better.