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

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

*Autor correspondiente de este trabajo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

6 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Páginas (desde-hasta)838-846
Número de páginas9
PublicaciónFuture Generation Computer Systems
Volumen102
DOI
EstadoPublicada - ene 2020

Nota bibliográfica

Publisher Copyright:
© 2019 Elsevier B.V.

Palabras clave

  • Online review
  • Sentiment analysis
  • Support Vector Machines
  • Weighting optimization

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