TY - CPAPER U1 - Konferenzveröffentlichung A1 - Wartena, Christian A1 - Sander, Uwe A1 - Patzelt, Christiane ED - Dobnik, Simon ED - Chatzikyriakidis, Stergios ED - Demberg, Vera T1 - Sentiment Independent Topic Detection in Rated Hospital Reviews T2 - Proceedings of the 13th International Conference on Computational Semantics - Short Papers N2 - 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. KW - Information Retrieval KW - Benutzererlebnis KW - Latent Semantic Analysis KW - Krankenhaus Y1 - 2019 UN - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-20766 UR - https://aclanthology.org/W19-0509.pdf U6 - https://doi.org/10.25968/opus-2076 DO - https://doi.org/10.25968/opus-2076 SP - 59 EP - 64 PB - Association for Computational Linguistics ER -