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        <title>Exploring Optimism and Pessimism in Twitter Using Deep Learning</title>
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        <namePart type="given">Cornelia</namePart>
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        <namePart type="given">Liviu</namePart>
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        <namePart type="family">Dinu</namePart>
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    <name type="personal">
        <namePart type="given">Bogdan</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1067</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Exploring Optimism and Pessimism in Twitter Using Deep Learning
%A Caragea, Cornelia
%A Dinu, Liviu P.
%A Dumitru, Bogdan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F caragea-etal-2018-exploring
%X Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.
%R 10.18653/v1/D18-1067
%U https://aclanthology.org/D18-1067
%U https://doi.org/10.18653/v1/D18-1067
%P 652-658

Markdown (Informal)

[Exploring Optimism and Pessimism in Twitter Using Deep Learning](https://aclanthology.org/D18-1067) (Caragea et al., EMNLP 2018)

• Exploring Optimism and Pessimism in Twitter Using Deep Learning (Caragea et al., EMNLP 2018)

ACL

• Cornelia Caragea, Liviu P. Dinu, and Bogdan Dumitru. 2018. Exploring Optimism and Pessimism in Twitter Using Deep Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 652–658, Brussels, Belgium. Association for Computational Linguistics.