药物评价数据集数据集

The dataset provides patient reviews on specific drugs along with related conditions. Furthermore, reviews are grouped into reports on the three aspects benefits, side effects and overall comment. Additionally, ratings are available concerning overall satisfaction as well as a 5 step side effect rating and a 5 step effectiveness rating. The data was obtained by crawling online pharmaceutical review sites. The intention was to study (1) sentiment analysis of drug experience over multiple facets, i.e. sentiments learned on specific aspects such as effectiveness and side effects, (2) the transferability of models among domains, i.e. conditions, and (3) the transferability of models among different data sources (see 'Drug Review Dataset (Drugs.com)'). The data is split into a train (75%) a test (25%) partition (see publication) and stored in two .tsv (tab-separated-values) files, respectively. important notes: When using this dataset, you agree that you 1) only use the data for research purposes 2) don't use the data for any commerical purposes 3) don't distribute the data to anyone else 4) cite us Attribute Information: 1. urlDrugName (categorical): name of drug 2. condition (categorical): name of condition 3. benefitsReview (text): patient on benefits 4. sideEffectsReview (text): patient on side effects 5. commentsReview (text): overall patient comment 6. rating (numerical): 10 star patient rating 7. sideEffects (categorical): 5 step side effect rating 8. effectiveness (categorical): 5 step effectiveness rating Relevant Papers: Felix Gr??er, Surya Kallumadi, Hagen Malberg, and Sebastian Zaunseder. 2018. Aspect-based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health (DH '18). ACM, New York, NY, USA, 121-125. DOI: [Web link] Citation Request: Felix Gr??er, Surya Kallumadi, Hagen Malberg, and Sebastian Zaunseder. 2018. Aspect-based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning. In Proceedings of the 2018 International Conference on Digital Health (DH '18). ACM, New York, NY, USA, 121-125. DOI: [Web link] Surya Kallumadi Kansas State University Manhattan, Kansas, USA surya '@' ksu.edu Felix Gr??er Institut für Biomedizinische Technik Technische Universit?t Dresden Dresden, Germany felix.graesser '@' tu-dresden.de