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.
Bibliographical noteFunding Information:
This research work is partially supported by the Spanish Government project TIN2017-89517-P , and a grant from the Fondo Europeo de Desarrollo Regional (FEDER) . Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva Formación ( FJCI-2016-28353 ). Sebastián Maldonado gratefully acknowledges financial support from CONICYT PIA/BASAL AFB180003 and FONDECYT -Chile, grant 1160738 .
This research work is partially supported by the Spanish Government projectTIN2017-89517-P, and a grant from the Fondo Europeo de Desarrollo Regional (FEDER). Eugenio Mart?nez C?mara was supported by the Spanish Government Programme Juan de la Cierva Formaci?n (FJCI-2016-28353). Sebasti?n Maldonado gratefully acknowledges financial support from CONICYTPIA/BASALAFB180003 and FONDECYT -Chile, grant 1160738.
© 2019 Elsevier B.V.
- Online review
- Sentiment analysis
- Support vector machines
- Weighting optimization