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        <title>A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks</title>
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        <namePart type="given">Soujanya</namePart>
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            <namePart type="given">Yuji</namePart>
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    <abstract>Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.</abstract>
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%0 Conference Proceedings
%T A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
%A Poria, Soujanya
%A Cambria, Erik
%A Hazarika, Devamanyu
%A Vij, Prateek
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F poria-etal-2016-deeper
%X Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.
%U https://aclanthology.org/C16-1151
%P 1601-1612

Markdown (Informal)

[A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://aclanthology.org/C16-1151) (Poria et al., COLING 2016)

• A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (Poria et al., COLING 2016)

ACL

• Soujanya Poria, Erik Cambria, Devamanyu Hazarika, and Prateek Vij. 2016. A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1601–1612, Osaka, Japan. The COLING 2016 Organizing Committee.