We propose a method to analyze public opinion about political issues online by automatically detecting polarity in Twitter data. Previous studies have focused on the polarity classification of individual tweets. However, to understand the direction of public opinion on a political issue, it is important to analyze the degree of polarity on the major topics at the center of the discussion in addition to the individual tweets. The first stage of the proposed method detects polarity in tweets using the Lasso and Ridge models of shrinkage regression. The models are beneficial in that the regression results provide sentiment scores for the terms that appear in tweets. The second stage identifies the major topics via a latent Dirichlet analysis (LDA) topic model and estimates the degree of polarity on the LDA topics using term sentiment scores. To the best of our knowledge, our study is the first to predict the polarities of public opinion on topics in this manner. We conducted an experiment on a mayoral election in Seoul, South Korea and compared the total detection accuracy of the regression models with five support vector machine (SVM) models with different numbers of input terms selected by a feature selection algorithm. The results indicated that the performance of the Ridge model was approximately 7% higher on average than that of the SVM models. Additionally, the degree of polarity on the LDA topics estimated using the proposed method was compared with actual public opinion responses. The results showed that the polarity detection accuracy of the Lasso model was 83%, indicating that the proposed method was valid in most cases.
Bibliographical noteFunding Information:
This work was supported by the Yonsei University Future-leading Research Initiative of 2014 (2014-22-0116). In addition, this work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046711).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Library and Information Sciences