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        <title>‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection</title>
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        <namePart type="given">Aditya</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Mark</namePart>
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            <title>Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)</title>
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            <namePart type="given">Eduardo</namePart>
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    <abstract>Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports the first topic model for sarcasm detection, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as ‘work’, ‘gun laws’, ‘weather’ are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25%.</abstract>
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%0 Conference Proceedings
%T ‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
%A Joshi, Aditya
%A Jain, Prayas
%A Bhattacharyya, Pushpak
%A Carman, Mark
%Y Blanco, Eduardo
%Y Morante, Roser
%Y Saurí, Roser
%S Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F joshi-etal-2016-thought
%X Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports the first topic model for sarcasm detection, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as ‘work’, ‘gun laws’, ‘weather’ are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25%.
%U https://aclanthology.org/W16-5001
%P 1-10

Markdown (Informal)

[‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection](https://aclanthology.org/W16-5001) (Joshi et al., EXprom 2016)

• ‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection (Joshi et al., EXprom 2016)

ACL

• Aditya Joshi, Prayas Jain, Pushpak Bhattacharyya, and Mark Carman. 2016. ‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection. In Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM), pages 1–10, Osaka, Japan. The COLING 2016 Organizing Committee.