Abstract
The most popular form of communication over the internet is text. There are wide range of services that allow users to communicate in the natural language using text messages. Twitter is one such popular Micro-blogging platform where users post their thoughts, feeling or opinion on a day-to-day basis. These text messages not only contain information about events, products and others but also the writer’s attitude. This kind of text data is useful to develop systems, which detect user emotions. Emotion detection has wide variety of applications including customer service, public policy making, education, future technology, and psychotherapy. In this work, we use Support Vector Machine classifier model to automatically classify user emotions. We achieve accuracy in the range of 88%. The Emotional information mined from such data is huge and these findings can be more useful if the system is able to provide some actionable recommendations to the user, which help them, achieve their goal and gain benefits. The recommendations or patterns are Actionable if user can perform action using the patterns to their advantage. Action Rules help discover ways to reclassify objects with respect to a specific target, which the user intends to change for their benefits. In this work, we focus on extracting Action Rules with respect to the Emotion class from user tweets. We discover actionable recommendations, which suggests ways to alter the user’s emotion to a better or more positive state.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M.-W. Dictionary, “Merriam-webster” (2002). http://www.mw.com/home.htm
Honey, C., Herring, S.C.: Beyond microblogging: conversation and collaboration via twitter. In 42nd Hawaii International Conference on System Sciences, 2009. HICSS 2009, pp. 1–10. IEEE (2009)
Chang, Y., Tang, L., Inagaki, Y., Liu, Y.: What is tumblr: a statistical overview and comparison. ACM SIGKDD Explor. Newsl. 16(1), 21–29 (2014)
Sullivan, D.: Comscore media metrix search engine ratings. Search Engine Watch 21 (2006)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56– 65. ACM (2007)
Chang, H.-C.: A new perspective on twitter hashtag use: diffusion of innovation theory. Proc. Assoc. Inf. Sci. Technol. 47(1), 1–4 (2010)
Hasan, M., Agu, E., Rundensteiner, E.: Using hashtags as labels for supervised learning of emotions in twitter messages. In: ACM SIGKDD Workshop on Health Informatics. New York, USA (2014)
Gupta, N., Gilbert, M., Fabbrizio, G.D.: Emotion detection in email customer care. Comput. Intell. 29(3), 489–505 (2013)
D’Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., Gleeson, J., Alvarez-Jimenez, M.: Artificial intelligence-assisted online social therapy for youth mental health. Front. Psychol. 8, 796 (2017)
Tantam, D.: The machine as psychotherapist: impersonal communication with a machine. Adv. Psychiatr. Treat. 12(6), 416–426 (2006)
Kaur, H.: Actionable rules: issues and new directions. In: World Enformatika Conference - WEC (5), pp. 61–64. Citeseer (2005)
He, Z., Xu, X., Deng, S., Ma, R.: Mining action rules from scratch. Expert Syst. Appl. 29(3), 691–699 (2005)
Mishne, G., et al.: Experiments with mood classification in blog posts. In: Proceedings of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, vol. 19, pp. 321–327 (2005)
Danisman, T., Alpkocak, A.: Feeler: emotion classification of text using vector space model. In: AISB 2008 Convention Communication, Interaction and Social Intelligence, vol. 1, p. 53 (2008)
Mohammad, S.M.: #emotional tweets. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pp. 246–255. Association for Computational Linguistics, Stroudsburg (2012)
Roberts, K., Roach, M.A., Johnson, J., Guthrie, J., Harabagiu, S.M.: Empatweet: annotating and detecting emotions on twitter. In: LREC, vol. 12, pp. 3806–3813. Citeseer (2012)
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)
Purver, M., Battersby, S.: Experimenting with distant supervision for emotion classification. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 482–491. Association for Computational Linguistics (2012)
Ranganathan, J., Irudayaraj, A.S., Tzacheva, A.A.: Action rules for sentiment analysis on twitter data using spark. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 51–60, November 2017
Makice, K.: Twitter API: Up and Running: Learn How to Build Applications with the Twitter API. O’Reilly Media, Inc., Beijing (2009)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word– emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. Association for Computational Linguistics (2010)
Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31(2), 301–326 (2015)
Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al.: A practical guide to support vector classification (2003)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415–425 (2002)
Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., et al.: Mllib: machine learning in apache spark. J. Mach. Learn. Res. 17(1), 1235–1241 (2016)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)
Ranganathan, J., Hedge, N., Irudayaraj, A., Tzacheva, A.: Automatic detection of emotions in twitter data - a scalable decision tree classification method. In: Proceedings of the RevOpID 2018 Workshop on Opinion Mining, Summarization and Diversification in 29th ACM Conference on Hypertext and Social Media (2018)
Tzacheva, A.A., Sankar, C.C., Ramachandran, S., Shankar, R.A.: Support confidence and utility of action rules triggered by meta-actions. In: 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA), pp. 113–120. Singapore (2016). https://doi.org/10.1109/ickea.2016.7803003
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tzacheva, A., Ranganathan, J., Mylavarapu, S.Y. (2020). Actionable Pattern Discovery for Tweet Emotions. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-20454-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20453-2
Online ISBN: 978-3-030-20454-9
eBook Packages: EngineeringEngineering (R0)