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Emotion Detection on Twitter Data using Knowledge Base Approach

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors:
Srinivasu Badugu, Matla Suhasini
10.5120/ijca2017913366

Srinivasu Badugu and Matla Suhasini. Emotion Detection on Twitter Data using Knowledge Base Approach. International Journal of Computer Applications 162(10):28-33, March 2017. BibTeX

@article{10.5120/ijca2017913366,
	author = {Srinivasu Badugu and Matla Suhasini},
	title = {Emotion Detection on Twitter Data using Knowledge Base Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {10},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {28-33},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume162/number10/27281-2017913366},
	doi = {10.5120/ijca2017913366},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. Social media tools like twitter is increasingly used by individuals to broadcast their day-to-day happenings or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better to interpret millions of individuals. This paper describes a Rule Based approach, which detects the emotion or mood of the tweet and classifies the twitter message under appropriate emotional category. The accuracy with the system is 85%. With the proposed system it is possible to understand the deeper levels of emotions i.e., finer grained instead of sentiment i.e., coarse grained. Sentiment says whether the tweet is positive or negative but the proposed system gives the deeper information of tweet which has adverse uses in the field of Psychology, Intelligence Bureau, Social and Economic trends.

References

  1. Maryam Hasan, Elke Rundensteiner, and Emmanuel Agu, May 2014, “EMOTEX: Detecting Emotions in Twitter Messages,” ASE BIGDATA/SOCIALCOM/CYBERSECURITY Conference, 27-31.
  2. Johan Bollen, Huina Mao, and Alberto Pepe, 2011, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena,” in International AAAI Conference on Weblogs and Social Media (ICWSM'11).
  3. Mike Thelwall, Kevan Buckley, and Georgios Paltoglou, 2007, “Sentiment in twitter events,” Journal of the American Society Tavel, Modeling and Simulation Design. AK Peters Ltd, P.
  4. Ed Diener and Martin E. P. Seligman, 2004, “Beyond money: toward an economy of well-being,” in PSYCHOLOGICAL SCIENCE IN THE PUBLIC INTEREST, American Psychological Society.
  5. Ed Diener, 2009, Assessing well-being: The collected works of Ed Diener, vol. 3, Springer,
  6. Shigehiro Oishi Ed Diener Ed Diener, “Subjective wellbeing: The science of happiness and life satisfaction,”
  7. Minsu Park, Chiyoung Cha, and Meeyoung Cha, 2012, “Depressive moods of users portrayed in twitter,” in Proc. of the ACM SIGKDD Workshop on Healthcare Informatics, HI-KDD.
  8. Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz , 2013, “Predicting depression via social media.,” in International AAAI Conference on Weblogs and Social Media (ICWSM'13), The AAAI Press.
  9. Golder S, Loke YK, Bland M, 2011, Meta-analyses of Adverse Effects Data Derived from Randomized Controlled Trials as Compared to Observational Studies: Methodological Overview. PLoS Med 8(5): e1001026. doi:10.1371/journal.pmed.1001026.
  10. Munmun De Choudhury, Scott Counts, and Michael Gamon, 2012, “Not all moods are created equal! Exploring human emotional states in social media,” in Sixth International AAAI Conference on Weblogs and Social Media (ICWSM'12).
  11. Carlo Strapparava and Rada Mihalcea, 2008, “Learning to identify emotions in text,” in Proceedings of the 2008 ACM symposium on Applied computing. ACM, pp.1556-1560.
  12. M. Naaman, J. Boase, and C.-H. Lai. 2010, Is it Really About Me? Message Content in Social Awareness Streams. In ACM Conference on Computer Supported Cooperative Work (CSCW).
  13. D. Kleinbaum, L. Kupper, and K. Muller. 2007, Applied regression analysis and other multivariable methods. Duxbury Pr.
  14. Go, A., Bhayani, R., & Huang L. 2009, Twitter Sentiment Classification Using Distant Supervision. Retrieved December 6, 2014, from http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf
  15. J. A. Russell, 1980, “A circumplex model of affect, 1980,” Journal of Personality and Social Psychology, vol. 39, pp. 1161-1178.
  16. http://sentiwordnet.isti.cnr.it

Keywords

Mood Detection, Emotion, Natural Language Processing, POS Tagging.