SACI: Sentiment analysis by collective inspection on social media content

Abstract

Collective opinions observed in Social Media represent valuable information for a range of applications. On the pursuit of such information, current methods require a prior knowledge of each individual opinion to determine the collective one in a post collection. Differently, we assume that collective analysis could be better performed when exploiting overlaps among distinct posts of the collection. Thus, we propose SACI (Sentiment Analysis by Collective Inspection), a lexicon-based unsupervised method that extracts collective sentiments without concerning with individual classifications. SACI is based on a directed transition graph among terms of a post set and on a prior classification of these terms regarding their roles in consolidating opinions. Paths represent subsets of posts on this graph and the collective opinion is defined by traversing all paths. Besides demonstrating that collective analysis outperforms individual one w.r.t. approximating collection opinions, assessments on SACI show that good individual classifications do not guarantee good collective analysis and vice-versa. Further, SACI fulfills simultaneously requirements of efficacy, efficiency and handle of dynamicity posed by high demanding scenarios. Indeed, the consolidation of a SACI-based Web tool for real-time analysis of tweets evinces the usefulness of this work.