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        <title>Sentiment Independent Topic Detection in Rated Hospital Reviews</title>
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        <namePart type="given">Christian</namePart>
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    <name type="personal">
        <namePart type="given">Uwe</namePart>
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            <namePart type="given">Stergios</namePart>
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    <abstract>We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.</abstract>
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%0 Conference Proceedings
%T Sentiment Independent Topic Detection in Rated Hospital Reviews
%A Wartena, Christian
%A Sander, Uwe
%A Patzelt, Christiane
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Short Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F wartena-etal-2019-sentiment
%X We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.
%R 10.18653/v1/W19-0509
%U https://aclanthology.org/W19-0509
%U https://doi.org/10.18653/v1/W19-0509
%P 59-64

Markdown (Informal)

[Sentiment Independent Topic Detection in Rated Hospital Reviews](https://aclanthology.org/W19-0509) (Wartena et al., IWCS 2019)

• Sentiment Independent Topic Detection in Rated Hospital Reviews (Wartena et al., IWCS 2019)

ACL

• Christian Wartena, Uwe Sander, and Christiane Patzelt. 2019. Sentiment Independent Topic Detection in Rated Hospital Reviews. In Proceedings of the 13th International Conference on Computational Semantics - Short Papers, pages 59–64, Gothenburg, Sweden. Association for Computational Linguistics.