Text Mining Facebook Status Updates for Sentiment Classification

被引:0
作者
Akaichi, Jalel [1 ]
Dhouioui, Zeineb [1 ]
Lopez-Huertas Perez, Maria Jose [2 ]
机构
[1] Inst Superieur Gest Tunis ISG, Dept Comp Sci, 41 Rue Liberte, Le Bardo 2000, Tunisia
[2] Univ Granada, Fac Biblioteconomi & Documentac, Granada 18071, Spain
来源
2013 17TH INTERNATIONAL CONFERENCE ON SYSTEM THEORY, CONTROL AND COMPUTING (ICSTCC) | 2013年
关键词
social networks; sentiment analysis; machine learning; naive Bayes;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, text mining and sentiment analysis have received great attention due to the abundance of opinion data that exist in social networks such as Facebook, Twitter, etc. Sentiments are projected on these media using texts for expressing feelings such as friendship, social support, anger, happiness, etc. Existing sentiment analysis studies tend to identify user behaviors and state of minds but remain insufficient due to complexities in conveyed texts. In this research paper, we focus on the usage of text mining for sentiment classification. Illustration is performed on Tunisian users' statuses on Facebook posts during the "Arabic Spring" era. Our aim is to extract useful information, about users' sentiments and behaviors during this sensitive and significant period. For that purpose, we propose a method based on Support Vector Machine (SVM) and Naive Bayes. We also construct a sentiment lexicon, based on the emoticons, interjections and acronyms', from extracted statuses updates. Moreover, we perform some comparative experiments between two machine learning algorithms SVM and Naive Bayes through a training model for sentiment classification.
引用
收藏
页码:640 / 645
页数:6
相关论文
共 28 条
[1]  
[Anonymous], 1996, ADV KNOWLEDGE DISCOV
[2]  
[Anonymous], P C HUM LANG TECHN E
[3]  
[Anonymous], 2002, P 40 ANN M ASS COMP
[4]  
[Anonymous], 1997, MACHINE LEARNING, MCGRAW-HILL SCIENCE/ENGINEERING/MATH
[5]  
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
[6]  
Carpenter B., 2005, P 2005 ASS COMPUTATI, P1
[7]  
Durant K. T., 2006, P WEBKDD 06
[8]  
ESULI A., 2005, Proceedings of ACM SIGIR Conference on Information and Knowledge Management (CIKM-05), P617
[9]  
Feldman R., 1995, TEXT MINING HDB ADV
[10]  
Go A., 2009, P CS224N PROJ REP ST