CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Multi-Class Twitter Emotion Classification: A New Approach

by R C Balabantaray, Mudasir Mohammad, Nibha Sharma
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 1
Year of Publication: 2012
Authors: R C Balabantaray, Mudasir Mohammad, Nibha Sharma
10.5120/ijais12-450651

R C Balabantaray, Mudasir Mohammad, Nibha Sharma . Multi-Class Twitter Emotion Classification: A New Approach. International Journal of Applied Information Systems. 4, 1 ( September 2012), 48-53. DOI=10.5120/ijais12-450651

@article{ 10.5120/ijais12-450651,
author = { R C Balabantaray, Mudasir Mohammad, Nibha Sharma },
title = { Multi-Class Twitter Emotion Classification: A New Approach },
journal = { International Journal of Applied Information Systems },
issue_date = { September 2012 },
volume = { 4 },
number = { 1 },
month = { September },
year = { 2012 },
issn = { 2249-0868 },
pages = { 48-53 },
numpages = {9},
url = { https://www.ijais.org/archives/volume4/number1/268-0651/ },
doi = { 10.5120/ijais12-450651 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:46:52.695108+05:30
%A R C Balabantaray
%A Mudasir Mohammad
%A Nibha Sharma
%T Multi-Class Twitter Emotion Classification: A New Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 4
%N 1
%P 48-53
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Micro blogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life every day. Therefore micro blogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because micro blogging has appeared relatively recently, there are a few research works that are devoted to this topic. In this paper, we are focusing on using Twitter, the most popular micro blogging platform, for the task of Emotion analysis. We will show how to automatically collect a corpus for Emotion analysis and opinion mining purposes and then perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we will build a Emotion classifier that will be able to determine the emotion class of the person writing.

References
  1. http://svmlight. joachims. org/svm_multiclass. html
  2. G. Mishne. Experiments with Mood Classification in Blog Posts, In Proceedings of the Style2005: The 1st Workshop on Stylistic Analysis of Text for Information Access, SIGIR 2005; Salvador, Brazil; Aug 15-19, 2005. .
  3. Y. Jung, H. Park and S. H. Myaeng, A Hybrid Mood Classification Approach for Blog Text, Lecture Notes in Computer Science, 4099, pp. 1099-1103, 2006.
  4. H. Liu, H. Lieberman and T. Selker, A model of textual affect sensing using real-world knowledge, In Proceedings of the 2003 international conference on intelligent user interfaces, pp. 125- 132, 2003.
  5. C. O. Alm, D. Roth and R. Sproat. Emotions from Text: Machine Learning for Text-based Emotion Prediction, In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada, pp. 579-586, 2005.
  6. A. Neviarouskaya, H. Prendinger and M. Ishizuka, Textual Affect Sensing for Social and Expressive Online Communication, In Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction. pp. 218-229, 2007.
  7. C. M. Lee and S. S. Narayanan. Toward Detecting Emotions in Spoken Dialogs, Journal of the American Society for Information Science. IEEE Trans. on Speech and Audio Processing 13(2), pp. 293-303, 2005.
  8. WWW. Twitter. com
  9. Quirk, R. , Greenbaum, S. , Leech, G. , Svartvik, J. : A Comprehensive Grammar of the English Language. Longman, New York (1985)
  10. Wiebe, J. , Wilson, T. , Cardie, C. : Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)
  11. Martin,J. R. . White,P. R. R. :The Language of Evaluation : Appraisal in English, Palgrave, London (2005), http://grammatics. com/appraisal/
  12. Read, J. , Hope, D. , Carroll, J. : Annotating expressions of Appraisal in English. In: The Proc. of the ACL Linguistic Annotation,Workshop, Prague (2007)
  13. Whitelaw, C. , Garg, N. , Argamon, S. : Using Appraisal Taxonomies for Sentiment Analysis. In: Proc. of the 2nd Midwest Comp. , Linguistic Colloquium, Columbus (2005)
  14. Alm, C. O. , Roth, D. , Sproat, R. : Emotions from text: machine learning for text-based emotion prediction. In: Proc. of the Joint Conf. on Human Language Technology/Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 579– 586 (2005)
  15. Ekman, P. : An Argument for Basic Emotions. Cognition and Emotion. 6, 169–200 (1992)
  16. Read, J. : Recognising affect in text using pointwise mutual information. Master's thesis, University of Sussex (2004)
  17. Liu, H. , Lieberman, H. , Selker, T. : A Model of Textual Affect Sensing using Real-World Knowledge. In: Proc. of the Int'l Conf. on Intelligent User Interfaces (2003)
  18. Neviarouskaya, A. , Prendinger, H . , Ishizuka, M. : Analysis of affect expressed through the evolving language of online communication. In: Proc. of the 12th Int'l Conf. on Intelligent
  19. User Interfaces (IUI-07), Honolulu, Hawaii, pp. 278–281 (2007) Mihalcea, R. , Liu, H. : A corpus-based approach to finding happiness. In: The AAAI Spring Symposium on Computational Approaches to Weblogs, Stanford, CA (2006)
  20. http://infolab. tamu. edu/resources/
  21. Passonneau, R. J. : Measuring agreement on set-valued items (MASI) for semantic and pragmatic annotation. In: Proc. 5th Int'l Conf. on Language Resources and Evaluation (2006)
  22. http://nlp. stanford. edu/software/corenlp. shtml
  23. http://tartarus. org/martin/PorterStemmer/
  24. http://nlp. stanford. edu/software/stanford-dependencies. shtml
  25. http://www-sers. york. ac. uk/~mb55/msc/clinimet/week4/ kappash2. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Emotion Analysis Sentiment Analysis Opinion Mining Text Classification

Powered by PhDFocusTM