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Title
Sentiment Analysis of User-Generated Content on Drug Review Websites
Author(s)
Jin-Cheon, NaWai Yan Min Kyaing
Publication Year
2015-03-30
Abstract
This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.
Keyword
Sentiment classification; drug reviews; linguistic approach; health and medical domains
Journal Title
Journal of Information Science Theory and Practice
Citation Volume
3
ISSN
2287-9099
DOI
10.1633/JISTaP.2015.3.1.1
Files in This Item:
Thumbnail E1JSCH_2015_v3n1_6.pdf544.19 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 3 - No. 1
Type
Article
URI
https://repository.kisti.re.kr/handle/10580/8671
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