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Reseach Article

TexEmo: Conveying Emotion from Text- The Study

by Mukesh C. Jain, V. Y. Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 4
Year of Publication: 2014
Authors: Mukesh C. Jain, V. Y. Kulkarni
10.5120/14977-3196

Mukesh C. Jain, V. Y. Kulkarni . TexEmo: Conveying Emotion from Text- The Study. International Journal of Computer Applications. 86, 4 ( January 2014), 43-49. DOI=10.5120/14977-3196

@article{ 10.5120/14977-3196,
author = { Mukesh C. Jain, V. Y. Kulkarni },
title = { TexEmo: Conveying Emotion from Text- The Study },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 4 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number4/14977-3196/ },
doi = { 10.5120/14977-3196 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:22.861369+05:30
%A Mukesh C. Jain
%A V. Y. Kulkarni
%T TexEmo: Conveying Emotion from Text- The Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 4
%P 43-49
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Human Computer Interface for communication is a very powerful and most current area of research because the human world is getting more and more digitize and one wants the digital systems to behave like a human being. This requires the digital systems to imitate the human behavior accurately. Emotion is one aspect of human behavior which plays an important role in human feeling and decision making thus influencing the way people interact in the society. In human computer interaction, the computer interfaces need to recognize the emotion of the end users in order to exhibit a truly intelligent behavior. Human express the emotion in the form their facial expression, through their speech, and by writing text. The automatic identification of emotions in texts is important for applications such as: opinion mining and market analysis, affective computing, natural language interfaces, and e-learning environments, including educational games. This paper is mainly focused on to conveying the emotion expressed by a text documents. In simple way I can say that main aim of this study is to classify the emotion expressed by the text, based on pre-defined list of emotion i. e. Anger, Joy, Sad, Fear, Disgust, and Surprise. In order to collect the emotion evoking word, this study paper is mainly enlightened on ISEAR dataset, WPARD and Word-Net Affect dataset. A lot of attention is paid to the normalization of text and expand the knowledge base of emotion word. Uses of Vector Space Model for Information retrieval and classification.

References
  1. Taner Danisman and Adil Alpkocak, 2008. Feeler: Emotion Classification of Text Using Vector Space Model. AISB 2008 Convention, Scotland.
  2. Shrutiranjan Satapathy and Sumit Bhagwani, 2012. Capturing Emotion in Sentences. cse. iitk. ac. in, Kanpur
  3. Khairullah Kan, Baharum Baharudin, Aurangzeb Khan and Fazal-e-Malik, 2009. Mining Opinion from Text Documets: A Survey. 3rd IEEE International Conference on Digital Ecosystems and Technologies.
  4. Cecilia Ovesdotter Alm, Dan Roth and Richard Sproat. 2005. Emotions from text: Machine Learning for Text-based emotion prediction. HLT '05.
  5. Carlo Strapparava and Rada Mihalcea. 2008. Learning to Identify Emotions in Text. Proceedings of the 2008 ACM SAC'08
  6. Shoushan LI, Chengqing Zong and Xia WANG, 2007. Sentiment Classification through Combining Classifiers with Multiple Feature Sets. IEEE International Conference.
  7. Saima Aman and Stan Szpakowicz. 2007. Identifying Expressions of Emotion in Text. TSD'07 Proceedings of the 10th international conference on Text, Speech and Dialogue.
  8. Futoshi Sugimoto and Masahide Yoneyama. 2006. A method for classifying emotion of Text bsed on Emotion-Dictionaries for Emotional Reading. AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications.
  9. Hugo Liu, Henry Lieberman and Ted Selker. 2003. A model of textual affect sensing using real-world knowledge. Proceedings of the 8th International Conference on Intelligent User Interfaces IUI'03.
  10. Edward Chao-Chun Kao Chun-Chieh Liu, Ting-Hao Yang, Chang-Tai Hsieh, Von-Wun Soo, 2009. Towards Text-based Emotion Detection A Survey and Possible Improvements. IEEE International Conference on Information Management and Engineering ICIME.
  11. Chapter 2 – Information Retrieval Model http://comminfo. rutgers. edu/~aspoerri/InfoCrystal/Ch_2. ps
  12. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment Classi_cation using Machine Learning Techniques. EMNLP'02.
  13. Shilpa Arora, Elijah May_eld, Carolyn Penstein-Ros and Eric Nyberg. 2010. Sentiment Classi_cation using Automatically Extracted Subgraph Features. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.
  14. Vincent Ng, Sajib Dasgupta and S. M. Niaz Ari_n. 2006. Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews. Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions.
  15. C. Strapparava and A. Valitutti, 2004. WordNet-Affect: an affective extension of WordNet,. 4th International Conference on Language Resources and Evaluation (LREC ?04), pp. 1083–1086, Lisbon, Portugal.
  16. S. K. Shandilya, S. K. Jain, A. K. Nagar, 2011. Opinion Mining and Information Retrieval Techniques for E-Commerce. In Edited Book: Ambient Intelligence and Smart Environments: Trends and Perspectives", edited by Dr. Fulvio Mastrogiovanni & Nak-Young Chong, ISBN: 978-16-1692-857-5, to be published in 2011 by IGI Global (IGI) – Information Science Publishing, USA.
  17. Alexander Pak and Patrick Paroubek. 2011. Text Representation Using Dependency Tree Subgraphs for Sentiment Analysis. DASFAA Workshops'11.
  18. A McCallum and Kamal Nigam. 1998. A Comparison of Event Models for Naive Bayes Text Classication. AAAI-98 Workshop on Learning for Text Categorization.
  19. Harry Zhang. 2004. The Optimality of Naive Bayes. FLAIRS'04.
  20. Andrew Y. Ng and Michael I. Jordan. 2002. On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. NIPS'02
  21. "ISEAR Dataset",http://emotion-research. net/toolbox/toolboxdatabase
  22. "Porter Stemming", http://en. wikipedia. org/wiki/Stemming
  23. Mohammad O. Wedyan Aarti Singh 2013. On the Design and Implementation of an Efficient Information Retrieval System for Arabic Language. International Journal of Electronics and Electrical Engineering Vol. 1, No. 1, March 2013
Index Terms

Computer Science
Information Sciences

Keywords

Information Retrieval Vector Space Classification.