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        <title>Microblog Emotion Classification by Computing Similarity in Text, Time, and Space</title>
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        <namePart type="given">Anja</namePart>
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            <title>Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
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    <abstract>Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.</abstract>
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%0 Conference Proceedings
%T Microblog Emotion Classification by Computing Similarity in Text, Time, and Space
%A Summa, Anja
%A Resch, Bernd
%A Strube, Michael
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F summa-etal-2016-microblog
%X Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.
%U https://aclanthology.org/W16-4317
%P 153-162

Markdown (Informal)

[Microblog Emotion Classification by Computing Similarity in Text, Time, and Space](https://aclanthology.org/W16-4317) (Summa et al., PEOPLES 2016)

• Microblog Emotion Classification by Computing Similarity in Text, Time, and Space (Summa et al., PEOPLES 2016)

ACL

• Anja Summa, Bernd Resch, and Michael Strube. 2016. Microblog Emotion Classification by Computing Similarity in Text, Time, and Space. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 153–162, Osaka, Japan. The COLING 2016 Organizing Committee.