Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/112100


Title: Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions
Authors: 郭耀煌
Kuo, Yau-Hwang
Fu, M.-H.
Tsai, W.-H.
Lee, K.-R.
Chen, L.-Y.
Contributors: 資科系
Keywords: Data mining;Social networking (online);Social sciences;Classification methods;Integrated classification method;Integrated informations;Micro-blog;Opinion mining;Relaxation labeling;Sentiment analysis;Sentiment classification;Classification (of information)
Date: 2016-03
Issue Date: 2017-08-23 10:42:33 (UTC+8)
Abstract: Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.
Relation: Applied Intelligence, 44(2), 399-413
Data Type: article
DOI 連結: http://dx.doi.org/10.1007/s10489-015-0700-z
Appears in Collections:[資訊科學系] 期刊論文

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