Sentiment Analysis is a field of Natural Language Processing that analyses and extracts emotions from the text and classify them as positive, negative, or neutral. It is mainly used to analyze the customer’s engagement with the products or an organization, reviews of the tourists of places, application reviews, and views of the users from the company. More importantly, it is a powerful tool in assisting businesses in determining what consumers think of them and their products by taking large datasets through google reviews or from the comment sections of the social media post. Polarity categorization is an important part of sentiment analysis where the overall mood expressed by a paragraph, phrase, or word is referred to as its polarity. This polarity can be expressed numerically as a ‘sentiment score’.
#Problem Statement
In this project, I have implemented a tourist review sentiment analysis Model that helps classify the reviews of different destinations as either positive, negative, or neutral. The goal of this study is to determine the proportion of negative, positive, and neutral, which allow us to understand the satisfaction of the tourist while visiting Nepal.
I will be comparing two models named VADER sentiment analyzer and Roberta, through the pre-trained model hugging face. The data used in this project is scraped from google maps reviews using the python Beautiful Soup library.