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Data for use on Google Collab

Processed data from https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+(Druglib.com)

Source:

Surya Kallumadi Kansas State University Manhattan, Kansas, USA surya '@' ksu.edu

Felix Gräßer Institut für Biomedizinische Technik Technische Universität Dresden Dresden, Germany felix.graesser '@' tu-dresden.de

Data Set Information:

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 Gräßer, 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 Gräßer, 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]