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Sentiment Polarity Classification of Figurative Language: Exploring the Role of Irony-Aware and Multifaceted Affect Features

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

The presence of figurative language represents a big challenge for sentiment analysis. In this work, we address the task of assigning sentiment polarity to Twitter texts when figurative language is employed, with a special focus on the presence of ironic devices. We introduce a pipeline model which aims to assign a polarity value exploiting, on the one hand, irony-aware features, which rely on the outcome of a state-of-the-art irony detection model, on the other hand a wide range of affective features that cover different facets of affect exploiting information from various sentiment and emotion lexical resources for English available to the community, possibly referring to different psychological models of affect. The proposed method has been evaluated on a set of tweets especially rich in figurative language devices proposed as a benchmark in the shared task on “Sentiment Analysis of Figurative Language” at SemEval-2015. Experiments and results of feature ablation show the usefulness of irony-aware features and the impact of using different affective lexicons for the task.

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Notes

  1. 1.

    This tweet is part of the dataset used in the SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter [13]. It was labeled as having negative polarity (−1.8).

  2. 2.

    This tweet is part of the sarcastic tweets in the dateset of Riloff et al. [8].

  3. 3.

    This tweet is part of the dateset used in the SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter [13]. It was labeled as having positive polarity (+0.63).

  4. 4.

    http://alt.qcri.org/semeval2015/task11/.

  5. 5.

    https://github.com/ironyAware-SA/sentimentAnalysisFeatures.

  6. 6.

    http://www.clips.ua.ac.be/pages/pattern-en#sentiment.

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Acknowledgments

The National Council for Science and Technology (CONACyT Mexico) has funded the research work of the first author (Grant No. 218109/313683 CVU-369616). The work of Paolo Rosso has been partially funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).

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Authors and Affiliations

  1. Dipartimento di Informatica, University of Turin, Turin, Italy

    Delia Irazú Hernández Farías, Cristina Bosco & Viviana Patti

  2. PRHLT Research Center, Universitat Politècnica de València, Valencia, Spain

    Delia Irazú Hernández Farías & Paolo Rosso

Authors
  1. Delia Irazú Hernández Farías
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  2. Cristina Bosco
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  3. Viviana Patti
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  4. Paolo Rosso
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Correspondence to Delia Irazú Hernández Farías .

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  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Hernández Farías, D.I., Bosco, C., Patti, V., Rosso, P. (2018). Sentiment Polarity Classification of Figurative Language: Exploring the Role of Irony-Aware and Multifaceted Affect Features. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_4

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