ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Emotion and Sarcasm Identification of Posts from Facebook Data Using a Hybrid Approach


Affiliations
1 Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
     

   Subscribe/Renew Journal


Facebook has become the most important source of news and people's feedback and opinion about almost every daily topic. Facebook represents one of the largest and most dynamic datasets of user generated content. Facebook posts can express opinions on different topics. With this massive amount of information in Facebook, there has to be an automatic tool that can categorize these information based on emotions. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. When detecting sarcasm in social media posts, the various features that are especially inherent to Facebook must be considered with importance.

Keywords

Emotion, Sarcasm, Bipartite, Fuzzy, Conflicting Emotion Model.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Nadia F.F. da Silva, Eduardo R. Hruschka and Estevam R. Hruschka Jr., “Tweet Sentiment Analysis with Classifier Ensembles”, Decision Support Systems, Vol. 66, pp. 170-179, 2014.

  • Ivan Habernal, Tomas Ptacek and Josef Steinberger, “Supervised Sentiment Analysis in Czech Social Media”, Information Processing and Management, Vol. 50, No. 5, pp. 693-707, 2014.

  • C. Strapparava and R. Mihalcea, “Learning to Identify Emotions in Text”, Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1556-1560, 2008.

  • Ze-Jing Chuang and Chung-Hsien Wu, “Multi-Modal Emotion Recognition from Speech and Text”, Computational Linguistics and Chinese Language Processing, Vol. 9, No. 2, pp. 45-62, 2004.

  • E. Cambria, R. Speer, C. Havasi and A. Hussain, “SenticNet: A Publicly Available Semantic Resource for Opinion Mining”, Commonsense Knowledge: AAAI Fall Symposium, Vol. FS-10-02, pp. 14-18, 2010.

  • Vincenzo Loia and Sabrina Senatore, “A Fuzzy-Oriented Sentic Analysis to Capture the Human Emotion in Web-based Content”, Knowledge-Based Systems, Vol. 58, pp. 75-85, 2014.

  • Weiyuan Li and Hua Xu, “Text-based Emotion Classification using Emotion Cause Extraction”, Expert Systems with Applications, Vol. 41, No. 4, Part 2, pp. 1742-1749, 2014.

  • O. Tsur, D. Davidov and A. Rappoport, “ICWSM-A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews”, Proceedings of the Fourth International Conference on Weblogs and Social Media, pp. 162-169, 2010.

  • R. Gonzlez-Ibez, S. Muresan and N. Wacholder, “Identifying Sarcasm in Twitter: A Closer Look”, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, Vol. 2, pp. 581-586, 2011.

  • Raquel Justo, Thomas Corcoran, Stephanie M. Lukin, Marilyn Walker and M. Ines Torres, “Extracting Relevant Knowledge for the Detection of Sarcasm and Nastiness in the Social Web”, Knowledge-Based Systems, Vol. 69, pp. 124-133, 2014.

  • Benno Stein and Sven Meyer Zu Eissen, “Document Categorization with MajorClust”, Proceedings of 12th Workshop on Information Technology and Systems, 2002.

  • Robert Plutchik, “The Nature of Emotions”, American Scientist, Vol. 89, No. 4, pp. 344-350, 2001.

  • Liling Tan, “Pywsd: Python Implementations of Word Sense Disambiguation (WSD) Technologies [Software]”, https://github.com/alvations/pywsd, 2014.

  • Steven Bird, Ewan Klein and Edward Loper, “Natural Language Processing with Python”, O’Reilly, 2009.


Abstract Views: 289

PDF Views: 3




  • Emotion and Sarcasm Identification of Posts from Facebook Data Using a Hybrid Approach

Abstract Views: 289  |  PDF Views: 3

Authors

V. M. Raghavan
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
P. Mohana Kumar
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
R. Sundara Raman
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
Rajeswari Sridhar
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India

Abstract


Facebook has become the most important source of news and people's feedback and opinion about almost every daily topic. Facebook represents one of the largest and most dynamic datasets of user generated content. Facebook posts can express opinions on different topics. With this massive amount of information in Facebook, there has to be an automatic tool that can categorize these information based on emotions. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. When detecting sarcasm in social media posts, the various features that are especially inherent to Facebook must be considered with importance.

Keywords


Emotion, Sarcasm, Bipartite, Fuzzy, Conflicting Emotion Model.

References