A Review of Sentimental Analysis for Vaccine Dataset Using BI-LSTM Method

In the age of digital communication, the realm of public opinion, particularly on pressing issues like vaccination, has found its voice through myriad online platforms. Given the surge of discussions around vaccines, especially in the wake of the COVID-19 pandemic, there is an imperative need to decipher the underlying sentiments from these vast datasets. Sentiment analysis, a prominent branch of Natural Language Processing (NLP), provides the tools to extract, process, and categorize such sentiments. While traditional machine learning models have held their ground in sentiment analysis tasks, the intricate nature of human emotions and language patterns demands more refined techniques. The Bidirectional Long Short-Term Memory (BI-LSTM) model, an enhanced variant of recurrent neural networks, emerges as a promising candidate with its capability to capture contextual information from both past and future data points in sequences. This review paper delves into the application and efficacy of the BI-LSTM method for sentiment analysis on vaccine-related datasets. Through a comprehensive analysis, we evaluate its performance metrics, benefits, limitations, and its positioning against other prevalent models. The findings suggest that BI-LSTM holds significant potential in providing nuanced insights into public sentiment regarding vaccines, which is instrumental for stakeholders to craft informed strategies and communications.