A Comparative Study of Sentiment Analysis on Public Opinion Data Using BERT and LSTM Architecture Based on a Transformers Approach

Human being are able to express their thoughts, perspectives, and opinions on a variety of topics through online forums, blogs, social media sites, digital discussion forums, as well as other virtual spaces as a result of the lightning-fast growth of technological advancements like the Internet and the rise of social networks. These viewpoints may help businesses like hotels and the tourism industry in enhancing their commodities and amenities for the visitors they serve. People are able to learn considerable information from the analysis of public sentiment, thoughts, perceptions, opinions, and so on. Sentiment analysis is essential for exploring the public’s sentiment or opinion because the findings may provide us with insightful information. By using Deep Learning technology to analyze the emotional tenor of comments, it is now possible to quickly understand public opinion on hotel product preferences, advertising tactics, and financial goals. Sentiment Analysis is another Natural Language Processing (NLP) application that has encountered an immense surge in popularity over the past few years. Nevertheless, the difficulties in NLP are impeding sentiment analysis’s efficacy and accuracy. Deep learning models have been highlighted to be a promising response to NLP’s issues lately. This research compares the BERT model with the LSTM model to accomplish sentiment analysis objectives to overcome this limitation. We developed a customized unique data set based on the general consensus regarding hotel reviews to properly implement the aforementioned method. Last but not least, a comparative study between the BERT and LSTM models has been conducted on the experimental results for the input features.