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        <title>A Personalized Sentiment Model with Textual and Contextual Information</title>
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        <namePart type="given">Siwen</namePart>
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    <name type="personal">
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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    <abstract>In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person’s past expressions, and offer a better understanding of the sentiment from the expresser’s perspective. Additionally, we investigate how a person’s sentiment changes over time so that recent incidents or opinions may have more effect on the person’s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.</abstract>
    <identifier type="citekey">guo-etal-2019-personalized</identifier>
    <identifier type="doi">10.18653/v1/K19-1093</identifier>
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%0 Conference Proceedings
%T A Personalized Sentiment Model with Textual and Contextual Information
%A Guo, Siwen
%A Höhn, Sviatlana
%A Schommer, Christoph
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F guo-etal-2019-personalized
%X In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person’s past expressions, and offer a better understanding of the sentiment from the expresser’s perspective. Additionally, we investigate how a person’s sentiment changes over time so that recent incidents or opinions may have more effect on the person’s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.
%R 10.18653/v1/K19-1093
%U https://aclanthology.org/K19-1093
%U https://doi.org/10.18653/v1/K19-1093
%P 992-1001

Markdown (Informal)

[A Personalized Sentiment Model with Textual and Contextual Information](https://aclanthology.org/K19-1093) (Guo et al., CoNLL 2019)

• A Personalized Sentiment Model with Textual and Contextual Information (Guo et al., CoNLL 2019)

ACL

• Siwen Guo, Sviatlana Höhn, and Christoph Schommer. 2019. A Personalized Sentiment Model with Textual and Contextual Information. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 992–1001, Hong Kong, China. Association for Computational Linguistics.