Estimating sentiment orientation in social media for intelligence monitoring and analysis

This paper presents a computational approach to inferring the sentiment orientation of “social media” content (e.g., blog posts) which focuses on the challenges associated with Web-based analysis. The proposed methodology formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and uses this framework to develop a semi-supervised sentiment classifier that is well-suited for social media domains. In particular, the proposed algorithm is capable of combining prior knowledge regarding the sentiment orientation of a few documents and words with information present in unlabeled data, which is abundant online. We demonstrate the utility of the approach by showing it outperforms several standard methods for the task of inferring the sentiment of online movie reviews, and illustrate its potential for security informatics through a case study involving the estimation of Indonesian public sentiment regarding the July 2009 Jakarta hotel bombings.