Volltext-Downloads (blau) und Frontdoor-Views (grau)

Sentiment Independent Topic Detection in Rated Hospital Reviews

  • 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.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Christian WartenaORCiDGND, Uwe SanderORCiDGND, Christiane PatzeltORCiDGND
URN:urn:nbn:de:bsz:960-opus4-20766
URL:https://aclanthology.org/W19-0509.pdf
DOI:https://doi.org/10.25968/opus-2076
DOI original:https://doi.org/10.18653/v1/W19-0509
Parent Title (English):Proceedings of the 13th International Conference on Computational Semantics - Short Papers
Publisher:Association for Computational Linguistics
Editor:Simon Dobnik, Stergios Chatzikyriakidis, Vera Demberg
Document Type:Conference Proceeding
Language:English
Year of Completion:2019
Publishing Institution:Hochschule Hannover
Release Date:2021/09/21
Tag:Latent Semantic Analysis
GND Keyword:Information Retrieval; Benutzererlebnis; Krankenhaus
First Page:59
Last Page:64
Institutes:Fakult├Ąt III - Medien, Information und Design
DDC classes:020 Bibliotheks- und Informationswissenschaft
410 Linguistik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International