Abstract
Social networks have been an emerging technology for communication among billions of users. One of the most popular social networks is Twitter. The popularity of Twitter comes from its simplicity since it allows users to exchange messages of short length that does not exceed 140 characters and takes the form of tweets. In this paper, we propose a model for performing a classification of tweets posted by the Twitter user based on a mixture of the topic and sentiment of those tweets. The proposed approach is new in that it creates a model that combines the processes of topic and sentiment classification of tweets simultaneously. Therefore, with this model, one can categorize tweets according to their topics and simultaneously assign them into different sentiments categories. The topic of the tweets in the basic experiment of the proposed approach is classified into five main different categories including: “political”, “commercials”, “educational”, “religious”, and “sportive”. Meanwhile, the sentiment of those tweets is classified into three main different categories including “positive”, “negative”, “neutral”. The effectiveness of the proposed approach is demonstrated on a real dataset that consists of various extracted tweets with different categories of topics and opinions. The empirical results show that our approach is very powerful in categorizing tweets according to topics and simultaneously assigning them into different sentiments categories.
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Notes
- 1.
In this term matrix, a row refers to a candidate word/term and a column refers to a tweet document.
References
Yang, S., Kolcz, A., Schlaikjer, A., Gupta, P.: Large-scale high-precision topic modeling on Twitter. In: Proceedings of KDD14, 24–27 August 2014, New York, NY, USA (2014)
Alghamdi, R., Alfalqi, K.: A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(1) (2015)
Kharde, V.A., Sonawane, S.S.: Sentiment analysis of Twitter data: a survey of techniques. Int. J. Comput. Appl. (IJCA) 139(11), 5–15 (2016)
Twitter Terms of Service. https://twitter.com/en/tos
Llewellyn, C., Grover, C., Alex, B., Oberlander, J., Tobin, R.: Extracting a topic specific dataset from a Twitter archive. In: Proceedings of the 19th International Conference on Theory and Practice of Digital Libraries (TPDL 2015), pp. 364–367 (2015)
Balahur, A.: Sentiment analysis in social media texts. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Atlanta, Georgia, June 2013, pp. 120–128. \(\copyright \)2013 Association for Computational Linguistics (2013)
Wang, S., Manning, C.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of ACL (2012)
Batista, F., Ribeiro, R.: Sentiment analysis and topic classification: case study over spanish tweets. In: Proceedings of TASS 2012, Satellite Event of the SEPLN 2012 Conference, 7 September, Valencia, Spain (2012)
Anta, A.F., Chiroque, L., Morere, P., Santos, A.: Sentiment analysis and topic detection of Spanish tweets: a comparative study of NLP techniques. J. Proces. del Leng. Nat. 50, 45–52 (2013)
David, J.: Sentiment and topic classification of messages on Twitter and using the results to interact with Twitter users. Examensarbete 30 hp, Uppsala University, Mars 2016
Bak, J.Y., Lin, C.-Y. Oh, A.: Self-disclosure topic model for Twitter conversations. In: Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media, Baltimore, Maryland, USA, 27 June 2014, pp. 42–49. \(\copyright \)2014 Association for Computational Linguistics (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International World Wide Web conference (WWW-2005), 10–14 May 2005, Chiba, Japan (2005)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web (WWW 2007), 8–12 May 2007, Banff, Alberta, Canada, pp. 171–180 (2007)
Ashique Mahmood, A.S.M.: Literature Survey on Topic Modeling. Dept. of CIS, University of Delaware (2013)
The R Project for Statistical Computing. https://www.r-project.org/
RStudio open source and enterprise-ready professional software for R. https://www.rstudio.com/
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. J. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley, Boston (2005)
Hidalgo, J.M.G.: Text Mining in WEKA: Chaining Filters and Classifiers, January 2013
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Hassan, D. (2018). A Simultaneous Topic and Sentiment Classification of Tweets. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_3
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DOI: https://doi.org/10.1007/978-3-319-76357-6_3
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