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    <titleInfo>
        <title>We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter</title>
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    <name type="personal">
        <namePart type="given">Cynthia</namePart>
        <namePart type="family">Van Hee</namePart>
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        <namePart type="given">Els</namePart>
        <namePart type="family">Lefever</namePart>
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    <name type="personal">
        <namePart type="given">Véronique</namePart>
        <namePart type="family">Hoste</namePart>
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    <abstract>Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.</abstract>
    <identifier type="citekey">van-hee-etal-2018-usually</identifier>
    <identifier type="doi">10.1162/coli_a_00337</identifier>
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        <url>https://aclanthology.org/J18-4010</url>
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        <date>2018-12</date>
        <detail type="volume"><number>44</number></detail>
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            <start>793</start>
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%0 Journal Article
%T We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter
%A Van Hee, Cynthia
%A Lefever, Els
%A Hoste, Véronique
%J Computational Linguistics
%D 2018
%8 December
%V 44
%N 4
%I MIT Press
%C Cambridge, MA
%F van-hee-etal-2018-usually
%X Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.
%R 10.1162/coli_a_00337
%U https://aclanthology.org/J18-4010
%U https://doi.org/10.1162/coli_a_00337
%P 793-832

Markdown (Informal)

[We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter](https://aclanthology.org/J18-4010) (Van Hee et al., CL 2018)

• We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter (Van Hee et al., CL 2018)

ACL

• Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018. We Usually Don’t Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter. Computational Linguistics, 44(4):793–832.