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Fi-Senti: A Language-Independent Model for Figurative Sentiment Analysis

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Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

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Abstract

This paper focuses on identifying the polarity of figurative language in the very short text collected from Social Network Services. Although this topic is not new, most computer scientists have solved this issue by using natural language processing techniques. This seems difficult for non-native English speakers because they have to rely on heuristics in language. Therefore, our target in this work is to find a language-independent approach to solve the problem without using any semantic resources (e.g., dictionaries and ontologies). A statistical method based on two main features (i.e., (i) textual terms and (ii) sentimental patterns) is proposed to determine the sentiment degree of three popular types of figurative language (i.e., sarcasm, irony, and metaphor). We experimented on two Test sets with about 3,800 tweets and used Cosine similarity as the correlation measurement for evaluating the performance. The results show that our Fi-Senti model (Figurative Sentiment analysis model) well performs in determining the sentiment intensity of the figurative language with the best achievement is 0.8952 with sarcasm and 0.9011 with irony.

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Notes

  1. 1.

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

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Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6037297). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154).

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Authors and Affiliations

  1. Department of Computer Engineering, Chung-Ang University, Seoul, Korea

    Hoang Long Nguyen & Jason J. Jung

  2. Faculty of Information Technology, Vietnam Maritime University, Haiphong, Vietnam

    Trung Duc Nguyen

Authors
  1. Hoang Long Nguyen
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  2. Trung Duc Nguyen
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  3. Jason J. Jung
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Corresponding author

Correspondence to Jason J. Jung .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

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Nguyen, H.L., Nguyen, T.D., Jung, J.J. (2016). Fi-Senti: A Language-Independent Model for Figurative Sentiment Analysis. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-42345-6_23

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