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Reseach Article

Two Step Approach for Emotion Detection on Twitter Data

by Matla Suhasini, Srinivasu Badugu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 53
Year of Publication: 2018
Authors: Matla Suhasini, Srinivasu Badugu
10.5120/ijca2018917350

Matla Suhasini, Srinivasu Badugu . Two Step Approach for Emotion Detection on Twitter Data. International Journal of Computer Applications. 179, 53 ( Jun 2018), 12-19. DOI=10.5120/ijca2018917350

@article{ 10.5120/ijca2018917350,
author = { Matla Suhasini, Srinivasu Badugu },
title = { Two Step Approach for Emotion Detection on Twitter Data },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 179 },
number = { 53 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number53/29539-2018917350/ },
doi = { 10.5120/ijca2018917350 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:06.821822+05:30
%A Matla Suhasini
%A Srinivasu Badugu
%T Two Step Approach for Emotion Detection on Twitter Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 53
%P 12-19
%D 2018
%I Foundation of Computer Science (FCS), NY, 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”. In this paper we have proposed a method which detects the emotion or mood of the tweet and classify the twitter message under appropriate emotional category. Our approach is a two-step approach, it is so called as it uses two approaches for the classification process, one is Rule Based approach and the other is Machine Learning approach. The first approach is the Rule Based Approach (RBA), our minor contributions in this approach are pre-processing, tagging, feature selection and Knowledge base creation. Feature selection is based on tags. Our second approach is Machine Learning Approach (MLA), in this the classifier is based on supervised machine learning algorithm called Naïve Bayes which requires labeled data. Naïve Bayes is used to detect and classify the emotion of a tweet. The output of RBA is given to MLA as input because MLA requires labeled data which we have already created through RBA. We have compared the accuracies of both the approaches, observed that, with the rule based approach we are able to classify the tweets with accuracy around 85% and with the machine learning approach the accuracy is around 88%. Machine learning approach performance is better than rule based approach, the performance has been improved as we have removed the error data while training the model. The approaches are involved with the concepts of Natural Language Processing, Artificial Intelligence, and Machine Learning for the development of the system. Our major contributions in this paper are detection of emotion for non hashtagged data and the labeled data creation for machine learning approach without manual creation.

References
  1. Srinivasu Badugu, Matla Suhasini, March 2017, “Emotion Detection on Twitter Data using Knowledge Base Approach”, International Journal of Computer Applications, Volume 162 – No 10, pp. 0975 – 8887.
  2. Maryam Hasan, Elke Rundensteiner, and Emmanuel Agu, May 2014, “EMOTEX: Detecting Emotions in Twitter Messages,” ASE BIGDATA/SOCIALCOM/CYBERSECURITY Conference, 27-31.
  3. 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).
  4. 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.
  5. 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.
  6. Ed Diener, 2009, Assessing well-being: The collected works of Ed Diener, vol. 3, Springer.
  7. Shigehiro Oishi Ed Diener Ed Diener, “Subjective wellbeing: The science of happiness and life satisfaction,”
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. 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.
  13. 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).
  14. D. Kleinbaum, L. Kupper, and K. Muller. 2007, Applied regression analysis and other multivariable methods. Duxbury Pr.
  15. 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/TwitterDist antSupervision09.pdf
  16. J. A. Russell, 1980, “A circumplex model of affect, 1980,” Journal of Personality and Social Psychology, vol. 39, pp. 1161-1178.
  17. http://sentiwordnet.isti.cnr.it
  18. David H. Olson, Douglas H. Sprenkle, Candyce S. Russell, 1979, “Circumplex Model of Marital and Family System: I. Cohesion and Adaptability Dimensions, Family Types and Clinical Applications”, Wiley Online Library, Vol.18, Issue 1, pg. 3-28.
  19. Akshi Kumar and Teeja Mary Sebastian. 2012, “Sentiment Analysis on Twitter”, International Journal of Computer Science Issues, Vol. 9, Issue 4, No. 3, ISSN (Online): 1694-0814.
  20. Apoorv Agarwal Boyi Xie, Ilia Vovsha, Owen Rambow, Rebecca Passonneau, 2011, “Sentiment Analysis of Twitter Data”, Proceedings of the Workshop on Language in Social Media, pg. 30-38.
  21. Aamera Z.H.Khan, Mohammad Atique, V. M. Thakare, 2015, “Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis”, National Conference on “Advanced Technologies in Computing and Networking"-ATCON, Special Issue of International Journal of Electronics, Communication & Soft Computing Science and Engineering, ISSN: 2277-9477.
Index Terms

Computer Science
Information Sciences

Keywords

Emotion Natural Language Processing POS Tagging