TY - JOUR
T1 - Enhancing the classification of social media opinions by optimizing the structural information
AU - Vairetti, Carla
AU - Martínez-Cámara, Eugenio
AU - Maldonado, Sebastián
AU - Luzón, Victoria
AU - Herrera, Francisco
PY - 2020/1/1
Y1 - 2020/1/1
N2 - . 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.
AB - . 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.
KW - Online review
KW - Sentiment analysis
KW - Support vector machines
KW - Weighting optimization
KW - Online review
KW - Sentiment analysis
KW - Support vector machines
KW - Weighting optimization
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U2 - 10.1016/j.future.2019.09.023
DO - 10.1016/j.future.2019.09.023
M3 - Article
VL - 102
SP - 838
EP - 846
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
SN - 0167-739X
ER -