Enhancing the classification of social media opinions by optimizing the structural information

Carla Vairetti, Eugenio Martínez-Cámara, Sebastián Maldonado, Victoria Luzón, Francisco Herrera

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

. Sentiment Analysis is an extensively studied task, however an important aspect yet to study is the underlying structural information of opinions. An important aspect to tackle is the analysis underlying structural information of opinions. Social media is a great source of user opinions, which are structured in most of the cases in two sections: the title and the content or body of the opinion. We claim that the structure of social media opinions has useful information for the polarity classification task. We propose a model for optimizing the contribution of that underlying structural information for polarity classification. Our model is built by weighting the contribution of each section, title and body. We develop a modified Support Vector Machine that includes a weight parameter, which is optimized via a line-search strategy. We evaluate our proposal on three datasets of reviews from different domains written in two different versions of the Spanish language. The results show that our model outperforms the classification of the joint or individual classification of each section of the opinion. Therefore, our claim holds.
Original languageAmerican English
Pages (from-to)838-846
Number of pages9
JournalFuture Generation Computer Systems
Volume102
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Online review
  • Sentiment analysis
  • Support vector machines
  • Weighting optimization

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