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        <title>Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network</title>
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        <namePart type="given">Zi-Yi</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rebecca</namePart>
            <namePart type="family">Hwa</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
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    <abstract>Document-level sentiment classification is a fundamental problem which aims to predict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.</abstract>
    <identifier type="citekey">dou-2017-capturing</identifier>
    <identifier type="doi">10.18653/v1/D17-1054</identifier>
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%0 Conference Proceedings
%T Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network
%A Dou, Zi-Yi
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F dou-2017-capturing
%X Document-level sentiment classification is a fundamental problem which aims to predict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.
%R 10.18653/v1/D17-1054
%U https://aclanthology.org/D17-1054
%U https://doi.org/10.18653/v1/D17-1054
%P 521-526

Markdown (Informal)

[Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network](https://aclanthology.org/D17-1054) (Dou, EMNLP 2017)

• Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network (Dou, EMNLP 2017)

ACL

• Zi-Yi Dou. 2017. Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 521–526, Copenhagen, Denmark. Association for Computational Linguistics.