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SACI: Sentiment Analysis by Collective Inspection on Social Media Content

20 Pages Posted: 10 Jul 2018 First Look: Accepted

See all articles by Leonardo Rocha

Leonardo Rocha

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Fernando Mourão

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Thiago Silveira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Rodrigo Chaves

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Giovanni Sa

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Felipe Teixeira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Ramon Vieira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science

Renato Ferreira

Federal University of Minas Gerais (UFMG) - Department of Computer Science

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.

Keywords: Sentiment Analysis, Classification, Transition Graph

Suggested Citation

Rocha, Leonardo and Mourão, Fernando and Silveira, Thiago and Chaves, Rodrigo and Sa, Giovanni and Teixeira, Felipe and Vieira, Ramon and Ferreira, Renato, SACI: Sentiment Analysis by Collective Inspection on Social Media Content (2015). Journal of Web Semantics First Look 34_3_3. Available at SSRN: https://ssrn.com/abstract=3199195 or http://dx.doi.org/10.2139/ssrn.3199195

Leonardo Rocha (Contact Author)

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Fernando Mourão

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Thiago Silveira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Rodrigo Chaves

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Giovanni Sa

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Felipe Teixeira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Ramon Vieira

Universidade Federal de Sao Joao Del Rei - Department of Computer Science ( email )

Sao Joao Del Rei
Brazil

Renato Ferreira

Federal University of Minas Gerais (UFMG) - Department of Computer Science ( email )

Av. Antônio Carlos, 6627
Belo Horizonte, 31270-901
Brazil

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