The State-of-the-Art in Twitter Sentiment Analysis

David Zimbra, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen
2018 ACM Transactions on Management Information Systems  
Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the
more » ... d approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches. To address these research questions, we first briefly introduce TSA, and we describe commonly applied approaches and major motivations for recent research. We then review the TSA literature, discuss the unique challenges associated with TSA, and present a taxonomy of the techniques devised in prior studies to address these challenges. To assess the state-of-the-art in TSA, we then conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive Twitter data sets. Following the experiments, we perform an error analysis to uncover the root causes of commonly occurring classification errors made by the systems. Since TSA systems are often deployed to monitor Twitter and detect the occurrences of specific events, we then apply the top-performing systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation, and we provide suggestions to guide the design of the next generation of TSA approaches.
doi:10.1145/3185045 fatcat:fzpm7xhkyvd2newi2yp3gze7gm