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        <title>Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets</title>
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    <name type="personal">
        <namePart type="given">Cynthia</namePart>
        <namePart type="family">Van Hee</namePart>
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    <name type="personal">
        <namePart type="given">Els</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).</abstract>
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        <url>https://aclanthology.org/C16-1257</url>
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%0 Conference Proceedings
%T Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets
%A Van Hee, Cynthia
%A Lefever, Els
%A Hoste, Véronique
%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 van-hee-etal-2016-monday
%X Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).
%U https://aclanthology.org/C16-1257
%P 2730-2739

Markdown (Informal)

[Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets](https://aclanthology.org/C16-1257) (Van Hee et al., COLING 2016)

• Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets (Van Hee et al., COLING 2016)

ACL

• Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2016. Monday mornings are my fave :) #not Exploring the Automatic Recognition of Irony in English tweets. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2730–2739, Osaka, Japan. The COLING 2016 Organizing Committee.