i-manager Publications

A Pattern-Based Approach to Detect Irony in Twitter Sentiment Analysis

Kumar S.*

Department of Computer Science, Saffrony Institute of Technology, Gujarat, India.

Periodicity:July - December'2023
DOI : https://doi.org/10.26634/jpr.10.2.20354

Abstract

Twitter sentiment analysis poses challenges due to the informal language, limited character count, and prevalence of sarcasm, which can alter the polarity of messages. This paper presents a pattern-based approach to detect irony in Twitter sentiment analysis. By analyzing various types of irony and identifying their patterns, this paper proposes a methodology to improve the efficiency of sentiment analysis. Tweets are classified into different categories based on their sarcasm using a machine learning algorithm. The proposed approach involves feature extraction from tweets, including sentiment-related features, punctuation-related features, grammatical and phonetic features, and patternbased features. A hybrid pattern extraction with a classification model is employed to process tweet data and classify it as sarcastic or not. Experimental results demonstrate the effectiveness of the proposed approach in detecting sarcasm in tweets, with precision ranging from 84.6% to 98.1% across different classifier algorithms. This pattern-based approach offers promising results for enhancing sentiment analysis on Twitter and understanding the nuances of communication in social media discourse.

Keywords

Twitter, Tweets, Sarcasm Detection, Sentiment Analysis, Pattern-Based Approach, Machine Learning, Irony.

How to Cite this Article?

Kumar, S. (2023). A Pattern-Based Approach to Detect Irony in Twitter Sentiment Analysis. i-manager’s Journal on Pattern Recognition, 10(2), 19-26. https://doi.org/10.26634/jpr.10.2.20354

References

[1]. Akcora, C. G., Bayir, M. A., Demirbas, M., & Ferhatosmanoglu, H. (2010, July). Identifying breakpoints in public opinion. In Proceedings of the First Workshop on Social Media Analytics, (pp. 62-66).

[2]. Barbieri, F., Saggion, H., & Ronzano, F. (2014, June). Modelling sarcasm in twitter, a novel approach. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, (pp. 50-58).

[3]. Bharti, S. K., Babu, K. S., & Jena, S. K. (2015, August). Parsing-based sarcasm sentiment recognition in twitter data. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, (pp. 1373-1380).

[4]. Boia, M., Faltings, B., Musat, C. C., & Pu, P. (2013, September). A:) is worth a thousand words: How people attach sentiment to emoticons and words in tweets. In 2013 International Conference on Social Computing, (pp. 345-350). IEEE.

[5]. Burfoot, C., & Baldwin, T. (2009, August). Automatic satire detection: Are you having a laugh? In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, (pp. 161-164).

[6]. Davidov, D., Tsur, O., & Rappoport, A. (2010, July). Semi-supervised recognition of sarcasm in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, (pp. 107-116).

[7]. Ghosh, D., Guo, W., & Muresan, S. (2015, September). Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, (pp. 1003-1012).

[8]. Liebrecht, C. C., Kunneman, F. A., & van Den Bosch, A. P. J. (2013). The perfect solution for detecting sarcasm in tweets# not. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, (pp. 29-37).

[9]. Manuel, K., Indukuri, K. V., & Krishna, P. R. (2010, December). Analyzing internet slang for sentiment mining. In 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems, (pp. 9-11). IEEE.

[10]. Maynard, D. G., & Greenwood, M. A. (2014, March). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Language Resources and Evaluation Conference (LREC) (pp. 26-31).

[11]. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv (pp. 79-86).

[12]. Shamay-Tsoory, S. G., Tomer, R., & Aharon-Peretz, J. (2005). The neuroanatomical basis of understanding sarcasm and its relationship to social cognition. Neuropsychology, 19(3), 288–300.

[13]. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., & Demirbas, M. (2010, July). Short text classification in twitter to improve information filtering. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (pp. 841-842).

[14]. Tepperman, J., Traum, D., & Narayanan, S. (2006). Yeah right: Sarcasm recognition for spoken dialogue systems. In Ninth International Conference on Spoken Language Processing.

[15]. Tsur, O., Davidov, D., & Rappoport, A. (2010, May). ICWSM-A great catchy name: Semi-super vised recognition of sarcastic sentences in online product reviews. In Proceedings of the International AAAI Conference on Web and Social Media, 4, (1), 162-169.

[16]. Veale, T., & Hao, Y. (2010). Detecting ironic intent in creative comparisons. In ECAI 2010 (pp. 765-770). IOS Press.

A Pattern-Based Approach to Detect Irony in Twitter Sentiment Analysis

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