Sentiment Discovery of Social Messages Using Self-Organizing Maps

被引:0
作者
Hsin-Chang Yang
Chung-Hong Lee
Chun-Yen Wu
机构
[1] National University of Kaohsiung,Department of Information Management
[2] National Kaohsiung University of Science and Technology,Department of Electrical Engineering
来源
Cognitive Computation | 2018年 / 10卷
关键词
Sentiment analysis; Social network analysis; Text mining; Self-organizing map;
D O I
暂无
中图分类号
学科分类号
摘要
Introduction Predicting the sentiments and emotions of people from their texts is a critical issue in cognitive computing. The explosive growth of social network services has led to a tremendous increase of textual data, increasing the demand of the advanced analysis of these data. Sentiment analysis on textual social media data emerged in recent years to fulfill the needs of areas such as national security, business, politics, and economics; however, text messages from social networks are rather different from those of traditional text documents, especially in presentation style and lengths. Therefore, it is difficult but essential to develop an effective method to explore the sentiments of social messages. Methods In this study, we first applied a self-organizing map (SOM) algorithm to cluster social messages as well as sentiment keywords. An association discovery process was then applied to discover the associations between a message and some sentiment keywords, and the sentiment of a message was determined according to such associations. Results We performed experiments on collected Twitter messages and the results’ accuracy outperformed that of a similar approach. Conclusions A sentiment analysis approach based on SOMs was proposed. The associations between messages and keywords were derived using the proposed method. The novelty of this work arises from the adoption of association discovery process in sentiment analysis.
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页码:1152 / 1166
页数:14
相关论文
共 51 条
[1]  
Cambria E(2015)Sentic Computing Cogn Comput 7 183-185
[2]  
Hussain A(2017)SLT-Based ELM for Big Social Data Analysis Cogn Comput 9 259-274
[3]  
Oneto L(2017)Sentence-Level Emotion Detection Framework Using Rule-Based Classification Cogn Comput 9 868-894
[4]  
Bisio F(2003)Bibliography of Self-Organizing map (SOM) papers: 1998-2001 addendum Neural Comput Surv 3 1-156
[5]  
Cambria E(2008)Opinion Mining and Sentiment Analysis Found Trends Inf Retr 2 1-135
[6]  
Anguita D(2010)Affect detection: An interdisciplinary review of models, methods, and their applications IEEE Trans Affect Comput 1 18-37
[7]  
Asghar MZ(2012)Sentiment analysis and opinion mining: a survey Int J Adv Res Comput Sci Softw Eng 2 282-292
[8]  
Khan A(2014)Sentiment analysis algorithms and applications: A survey Ain Shams Eng J 5 1093-1113
[9]  
Bibi A(2016)Affective computing and sentiment analysis IEEE Intell Syst 31 102-107
[10]  
Kundi FM(2017)Review of affective computing: From unimodal analysis to multimodal fusion Inf Fusion 37 98-125