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Statistical approach for figurative sentiment analysis on Social Networking Services: a case study on Twitter

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Abstract

This paper presents a system that analyzes the sentiment of figurative language contained in short texts collected from Social Networking Services (SNS). This case study sources information from tweets on Twitter and calculates the polarity of the figurative language with three different categories (i.e., sarcastic, ironic, and metaphorical tweets). As in Medhat et al. (Ain Shams Eng J 5(4):1093–1113, 2014), Nguyen and Jung (Mob Netw Appl 20(4):475–486, 2015), many related works have used a lexical-based approach (e.g., dictionary and corpus), and machine learning-based approach (e.g., decision tree, rule discovery, and probabilistic methods) to extract sentiment in a given text. This statistical approach makes use of two main features: i) Content-based, and ii) Emotion Pattern-based. We believe that this combination offers a general method to solve the current problem and easily extends for analyzing other types of figurative languages. The proposed algorithm is evaluated by using Cosine similarity to conduct an experiment over a Data set that contains about 5,000 tweets. The results show that the FIS Model (Figurative language Identification using Statistical-based Model) works well with figurative tweets with a highest achievement of 0.7813.

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Notes

  1. http://en.wikipedia.org/wiki/Sarcasm.

  2. http://en.wikipedia.org/wiki/Irony.

  3. http://en.wikipedia.org/wiki/Metaphor.

  4. http://alt.qcri.org/semeval2015/.

References

  1. Aspili A (2013) Available at http://www.socialmediatoday.com/content/who-benefits-sentiment-analysis (Accessed 6 Apr 2016)

  2. Attardo S (2000) Irony as relevant inappropriateness. Pragmatics 32(6):793–826

    Article  Google Scholar 

  3. Bello-Orgaz G, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Inf Fusion 28:45–59

    Article  Google Scholar 

  4. Colston HL (2001) On necessary conditions for verbal irony comprehension. Pragmat Cogn 8(2):277–324

    Article  Google Scholar 

  5. Hao Y, Veale T (2010) An ironic fist in a velvet glove: creative mis-representation in the construction of ironic similes. Mind Mach 20(4):635–650

    Article  Google Scholar 

  6. Hoang Long N, Jung JJ (2015) Privacy-aware framework for matching online social identities in multiple social networking services. Cybern Syst 46(1–2):69–83

    Article  Google Scholar 

  7. Hong M, Jung JJ (2016) MyMovieHistory: social recommender system by discovering social affinities among users. Cybern Syst 47(1–2):88–110

    Article  Google Scholar 

  8. Jung JJ (2016) Exploiting geotagged resources for spatial clustering on social network services. Concurr Comput Pract Exp 28(4):1356–1367

    Article  Google Scholar 

  9. Kaur A, Gupta V (2013) A survey on sentiment analysis and opinion mining techniques. Emerg Technol Web Intell 5(4):367–371

    Google Scholar 

  10. Kumon-Nakamura S, Glucksberg S, Brown M (1995) How about another piece of pie: the allusional pretense theory of discourse irony. J Exp Psychol Gen 124(1):3–21

    Article  Google Scholar 

  11. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  12. Nguyen DT, Jung JE (2015) Real-time event detection on social data stream. Mob Netw Appl 20(4):475–486

    Article  Google Scholar 

  13. Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on Twitter: a behavioral modeling approach. In: Proceedings of the 8th ACM international conference on web search and data mining. Shanghai, pp 97–106

  14. Reyes A, Rosso P (2012a) Making objective decisions from subjective data: detecting irony in customers reviews. Decis Support Syst 53(4):754–760

    Article  Google Scholar 

  15. Reyes A, Rosso P, Buscaldi D (2012b) From humor recognition to irony detection: the figurative language of social media. Data Knowl Eng 74:1–12

    Article  Google Scholar 

  16. Reyes A, Rosso P (2013a) On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowl Inf Syst 40(3):595–614

    Article  Google Scholar 

  17. Reyes A, Rosso P, Veale T (2013b) A multidimensional approach for detecting irony in Twitter. Lang Resour Eval 47(1):239–268

    Article  Google Scholar 

  18. Shutova E, Sun L, Korhonen A (2010) Metaphor identification using verb and noun clustering. In: Proceedings of the 23rd international conference on computational linguistics (COLING 2010). Beijing, pp 1002–1010

  19. Veale T (2012) Detecting and generating ironic comparisons: an application of creative information retrieval. In: Proceedings of the 26th international conference on association for the advancement of artificial intelligence (AAAI 2012). Arlington

  20. Veale T, Hao Y (2007) Comprehending and generating apt metaphors: a web-driven, case-based approach to figurative language. In: Proceedings of the 22nd international conference on association for the advancement of artificial intelligence (AAAI 2007). Vancouver, pp 1471–1476

  21. Veale T, Keane MT (1992) Conceptual scaffolding: a spatially founded meaning representation for metaphor comprehension. Comput Intell 8(3):494–519

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154). Also, this research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Cetner) support program (NIPA-2014-H0301-14-1044) supervised by the NIPA (National ICT Industry Promotion Agency).

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  1. Department of Computer Science and Engineering, Chung-Ang University, Seoul, Korea, 156–756

    Hoang Long Nguyen & Jai E. Jung

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  1. Hoang Long Nguyen
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  2. Jai E. Jung
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Correspondence to Jai E. Jung.

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Nguyen, H.L., Jung, J.E. Statistical approach for figurative sentiment analysis on Social Networking Services: a case study on Twitter. Multimed Tools Appl 76, 8901–8914 (2017). https://doi.org/10.1007/s11042-016-3525-9

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  • DOI: https://doi.org/10.1007/s11042-016-3525-9

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