Sentimental analysis on user’s reviews using BERT

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In the current E-Commerce and M−Commerce evolved Internet world, people are expressing their sentiments on the product or service in the form of reviews and ratings. Sentiment analysis finds a contextual inference in the user sentiment which helps the E-Commerce sector and other end-users to realize the opinion of the product or service. People give their opinion in a variety of formats like reviews, blogs, news, or comments. Sentiment Analysis is used quick gaining insights using large volumes of text data which can be helpful in improving the quality of service and can make companies to earn huge profits. In sentimental analysis, modeling of sentimental relations such as word negation and word intensification are great challenge. In the proposed work, Bidirectional Encoder Representations from Transformers (BERT) is constructed for classifying user sentiments on IMDB Movie Review and Amazon Fine Food Review datasets. Word negations and intensifications are also considered for deriving the sentiment of reviews. The experimental results shows that the BERT can capture the sentimental relations effectively than basic ML, DL models and other recent literatures.