Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2013). Combining strengths, emotions and polarities for boosting Twitter sentiment analysis. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013 - Held in Conjunction with SIGKDD 2013. http://doi.org/10.1145/2502069.2502071
Permanent Research Commons link: https://hdl.handle.net/10289/10763
Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.
This is an author’s accepted version of an article published in proceedings of WISDOM'13, August 11 Chicago, U.S.A. © 2013 ACM.