Enhancingtheclassification of social mediaopinionsbyoptimizingthe structural information

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dc.creatorVairetti, Carla
dc.creatorMartínez-Cámarac, Eugenio
dc.creatorMaldonadoa, Sebastián
dc.date2021
dc.date.accessioned2026-03-12T11:45:00Z
dc.date.accessioned2026-03-12T16:28:43Z
dc.date.available2026-03-12T11:45:00Z
dc.date.available2026-03-12T16:28:43Z
dc.description.abstractSentiment 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.en
dc.identifierhttps://investigadores.uandes.cl/en/publications/f3c9e6a2-b972-481b-8cad-5a128cab76b8
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/1110004
dc.languagespa
dc.publisherUniversidad de los Andes
dc.subjectOnline review
dc.subjectSentiment analysis
dc.subjectSupport vector machines
dc.titleEnhancingtheclassification of social mediaopinionsbyoptimizingthe structural informationspa
dc.typeArticle
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