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.
| 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 |
| Link to catalogue: | 1776168488 |
| Institutes: | Fakultät III - Medien, Information und Design |
| DDC classes: | 020 Bibliotheks- und Informationswissenschaft |
| 410 Linguistik | |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
