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.
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References
Abbasi A, Chen H, Thoms S, Fu T (2008) Affect analysis of web forums and blogs using correlation ensembles. IEEE Trans Knowl Data Eng 20(9):1168–1180
Angelova R, Weikum G (2006) Graph-based Text Classification: Learn from Your Neighbors. ACM Proceedings, pp 485–492
Brody S, Diakopoulos N (2011) Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: Using Word Lengthening to Detect Sentiment in Microblogs. In: Proceedings Conference on Empirical Methods in Natural Language Processing, Edinburgh, United Kingdom, pp 562–570
Bermingham A, Smeaton AF (2010) Classifying Sentiment in Microblogs: Is Brevity an Advantage. Proceedings 19 th ACM Int’l Conference on Information and Knowledge Management, Toronto, Canada, pp 1833–1836
Boutet A, Kim H, Yoneki E (2012) What’s in Your Tweets? I Know Who You Supported in the UK 2010 General Election. Proceedings 16th Int’l AAAI Conference on Weblogs and Social Media
Cambria E, Schuller B, Liu B, Wang H, Havasi C (2013) Knowledge-based approaches to concept-level sentiment analysis. IEEE Intell Syst 28:12–14
Davidov D, Tsur O, Rappoport A (2010) Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. Proceedings 23rd Int’l Conference on Computational Linguistics, Beijing, China, pp 241–249
Gryc W, Moilanen K (2010) Leveraging Textual Sentiment Analysis with Social Network Modelling: Sentiment Analysis of Political Blogs in the 2008 U.S. Presidential Election. Proceedings From Text to Political Positions Workshop
Hatzivassiloglou V, Mckeown KR (1993) Towards the Automatic Identification of Adjectival Scales: Clustering Adjectives According to Meaning. Proceedings 31 st Annual Meeting of the Association for Computational Linguistics, Ohio, USA
Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent Twitter Sentiment Classification. Proceedings 49 th Annual Meeting of the Association for Computational Linguistics, Portland, Oregon, pp 151–160
Kaplan AM, Haenlein M (2011) The Early Bird Catches the News: Nine Things You Should Know about Micro-blogging. Business Horizons, pp 105–113
Kittler J, Illingworth J (1985) Relaxation labelling algorithms — a review. Image Vis Comput 3(11):206–216
Ku LW, Liang YT, Chen HH (2006) Tagging Heterogeneous Evaluation Corpora for Opinionated Tasks. Conference on Language Resources and Evaluation LREC, pp 667–670
Liang P, Dai B (2013) Opinion Mining on Social Media Data. Proceedings 14 th IEEE International Conference on Mobile Data Management
Liu B (2010) Sentiment Analysis and Subjectivity. Chapter 26, Handbook of Natural Language Processing, 2nd edn. Chapman and Hall, pp 627–666
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415– 444
Pang B, Lee L (2008) Opinion Mining and Sentiment Analysis. Found Trends Inf Retr 2:1–135
Pang B, Lee L (2004) A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization based on Minimum Cuts. Association for Computational Linguistics, Article No. 271
Pak A, Paroubek P (2010) Twitter as a Corpus for Sentiment Analysis and Opinion Mining:1320–1326
Riloff E, Patwardhan S, Wiebe J (2006) Feature Subsumption for Opinion Analysis. Proceedings Conference Empirical Methods in Natural Language Processing, Sydney, Australia , pp 440–448
Riloff E, Wiebe J (2003) Learning Extraction Patterns for Subjective Expressions. Proc. Conf. Empirical Methods in Natural Language Processing, pp 105–112
Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-Level Sentiment Analysis. Incorporating Social Networks Proceedings KDD?11, California, USA, pp 1397–1405
Tang H, Tan S, Cheng X (2009) A Survey on Sentiment Detection of Reviews. Expert Syst Appl 36:10760–10773
Terrana D, Augello A, Pilato G (2014) Facebook Users Relationships Analysis Based on Sentiment Classification. IEEE International Conference on Semantic Computing, pp 290–296
Thelwall M (2010) Emotion Homophily in Social Network Site Messages. First Monday. http://firstmonday.org/ojs/index.php/fm/article/view/2897/2483
Turney PD (2002) Thumbs up or Thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceedings Association for Computational Linguistics , pp 417–424
Turney PD, Littman ML (2003) Measuring Praise and Criticism: Inference of Semantic Orientation from Association. ACM Trans Inf Syst 21:315–346
West R, Paskov HS, Leskovec J, Potts C (2014) Exploiting social network structure for person-to-person sentiment analysis. In: Proceedings of Empirical Methods for Natural Language Processing (EMNLP)
Acknowledgments
This research was, in part, supported by the Ministry of Education, Taiwan, R.O.C. the aim for the Top University Project to the National Cheng Kung University (NCKU). This work was also supported in part by the National Science Council, Taiwan, under grant NSC-100-2221-E-006-251-MY3.
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Kuo, YH., Fu, MH., Tsai, WH. et al. Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions. Appl Intell 44, 399–413 (2016). https://doi.org/10.1007/s10489-015-0700-z
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DOI: https://doi.org/10.1007/s10489-015-0700-z