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        <title>Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters</title>
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        <namePart type="given">Fabio</namePart>
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        <namePart type="given">Evgeny</namePart>
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    <name type="personal">
        <namePart type="given">Massimo</namePart>
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    <name type="personal">
        <namePart type="given">Giuseppe</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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            <namePart type="given">Malvina</namePart>
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            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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    <abstract>On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.</abstract>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters
%A Celli, Fabio
%A Stepanov, Evgeny
%A Poesio, Massimo
%A Riccardi, Giuseppe
%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 celli-etal-2016-predicting
%X On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.
%U https://aclanthology.org/W16-4312/
%P 110-118

Markdown (Informal)

[Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters](https://aclanthology.org/W16-4312/) (Celli et al., PEOPLES 2016)

• Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters (Celli et al., PEOPLES 2016)

ACL

• Fabio Celli, Evgeny Stepanov, Massimo Poesio, and Giuseppe Riccardi. 2016. Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 110–118, Osaka, Japan. The COLING 2016 Organizing Committee.