Deep Sentiment: “An Effective Sentiment Analysis of Twitter Data using BERT+GRU Hybrid Model”

Sentiment Analysis (SA) has been an active area of research for over a decade. It involves an in-depth study of opinions, sentiments, and subjectivity expressed in text. Among various platforms, Twitter is one of the most popular microblogging services and serves as a significant source for sentiment analysis and data exploration. Organizations globally utilize SA on Twitter data to gain insights with applications across various domains. However, while SA is effective for understanding public opinion, bias in tweets can distort analysis, leading to inaccurate results and potentially influencing users to make flawed decisions. Such biased tweets may originate from both authentic but biased individuals and automated social bots that disseminate skewed opinions on specific topics. To address this challenge, this research proposes a statistical model designed to detect users and bots responsible for spreading biased content on Twitter. Using an annotated Twitter dataset, the study evaluates sentiment analysis results with and without biased tweets, analysing the impact of biased users at both micro and macro levels. Experimental results demonstrate that the proposed approach effectively distinguishes biased users and bots from genuine users, thereby enhancing the accuracy of sentiment analysis.