Evaluation of ensemble-based sentiment classifiers for Twitter data
Christos Troussas, Akrivi Krouska, Maria K. Virvou · 2016
Social media are widely used worldwide and offer the possibility to users to post real time messages respecting their opinions on different topics, discuss everyday issues, complain and express positive, neutral or negative sentiments for anything that concerns them. As such, sentiment analysis has become a burning issue in the scientific literature. However, some researchers argue that Twitter sentiment classification performance may be elusive. To overcome this issue, in this paper, we evaluate the most common ensemble methods that can be used for effective sentiment analysis and the tested datasets used in this research proceed from Twitter. Experiment results reveal that the use of ensembles of multiple base classifiers can improve the accuracy of Twitter sentiment analysis. The discussion that is presented can clearly prove that such methods can surprisingly surpass the traditional algorithms in performance and can be seen as a beneficial tool in the field of sentiment analysis that can further enhance several other domains such as e-learning and web advertising.