Estimating the sentiment of social media content for security informatics applications

Kristin Glass, Richard Colbaugh
2011 Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics  
Inferring the sentiment of social media content, for instance blog posts and forum threads, is both of great interest to security analysts and technically challenging to accomplish. This paper presents two computational methods for estimating social media sentiment which address the challenges associated with Web-based analysis. Each method formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and assumes that only limited prior
more » ... ited prior information is available regarding the sentiment orientation of any of the documents or words of interest. The first algorithm is a semi-supervised sentiment classifier which combines knowledge of the sentiment labels for a few documents and words with information present in unlabeled data, which is abundant online. The second algorithm assumes existence of a set of labeled documents in a domain related to the domain of interest, and leverages these data to estimate sentiment in the target domain. We demonstrate the utility of the proposed methods by showing they outperform several standard techniques for the task of inferring the sentiment of online movie and consumer product reviews. Additionally, we illustrate the potential of the methods for security informatics by estimating regional public opinion regarding two events: the 2009 Jakarta hotel bombings and 2011 Egyptian revolution. Authors' contributions KG and RC designed the research, KG and RC developed the computational algorithms, KG conducted the empirical tests, and RC wrote the paper. All authors read and approved the final manuscript.
doi:10.1109/isi.2011.5984052 dblp:conf/isi/GlassC11 fatcat:fjqgxyxyjrc3hlq5j7nfioivxy