{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:31:15Z","timestamp":1760596275666,"version":"3.41.2"},"reference-count":9,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2016,11,14]],"date-time":"2016-11-14T00:00:00Z","timestamp":1479081600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["OIR"],"published-print":{"date-parts":[[2016,11,14]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient\u2019s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper proposes a detection algorithm to a medical-opinion-mining system using rule-based and support vector machines (SVM) algorithms. A corpus from 225 drug reviews was manually annotated by a medical expert for training and testing.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The results show that SVM significantly outperforms a rule-based algorithm. However, the results of both algorithms are encouraging and a good foundation for future research. Obviating the limitations and exploiting combined approaches would improve the results.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>An automatic extraction for adverse drug effects information from online text can help regulatory authorities in rapid information screening and extraction instead of manual inspection and contributes to the acceleration of medical decision support and safety alert generation.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The results of this study can help database curators in compiling adverse drug effects databases and researchers to digest the huge amount of textual online information which is growing rapidly.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/oir-06-2015-0208","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T04:19:01Z","timestamp":1478233141000},"page":"1018-1032","source":"Crossref","is-referenced-by-count":14,"title":["Recognition of side effects as implicit-opinion words in drug reviews"],"prefix":"10.1108","volume":"40","author":[{"given":"Monireh","family":"Ebrahimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir Hossein","family":"Yazdavar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naomie","family":"Salim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Safaa","family":"Eltyeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"first-page":"81","article-title":"Context: an algorithm for identifying contextual features from clinical text","year":"2007","key":"key2020121120565944700_ref001"},{"key":"key2020121120565944700_ref002","unstructured":"Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A. and Damljanovic, D. (2011), \u201cDeveloping language processing components with gate version 6 (a user guide)\u201d, available at: http:\/\/gate.Ac.Uk\/sale\/tao\/tao. Pdf (accessed May 12, 2015)."},{"first-page":"548","article-title":"Textual and informational characteristics of health-related social media content: a study of drug review forums","year":"2011","key":"key2020121120565944700_ref004"},{"year":"2011","key":"key2020121120565944700_ref005"},{"volume-title":"Medical Data Mining: Improving Information Accessibility Using Online Patient Drug Reviews","year":"2011","key":"key2020121120565944700_ref006"},{"key":"key2020121120565944700_ref007","unstructured":"Liu, B. and Zhang, L. (2013), \u201cA survey of opinion mining and sentiment analysis\u201d, Mining Text Data, Springer, New York, NY, pp. 415-463."},{"issue":"5","key":"key2020121120565944700_ref008","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1136\/jamia.2010.003657","article-title":"Medication information extraction with linguistic pattern matching and semantic rules","volume":"17","year":"2010","journal-title":"Journal of The American Medical Informatics Association"},{"issue":"4","key":"key2020121120565944700_ref009","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.jbi.2010.03.011","article-title":"Selecting information in electronic health records for knowledge acquisition","volume":"43","year":"2010","journal-title":"Journal of Biomedical Informatics"},{"volume-title":"Sideffective-System to Mine Patient Reviews: Sentiment Analysis","year":"2011","key":"key2020121120565944700_ref010"}],"container-title":["Online Information Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/OIR-06-2015-0208","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-06-2015-0208\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-06-2015-0208\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:43:13Z","timestamp":1753396993000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/oir\/article\/40\/7\/1018-1032\/454102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,14]]},"references-count":9,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2016,11,14]]}},"alternative-id":["10.1108\/OIR-06-2015-0208"],"URL":"https:\/\/doi.org\/10.1108\/oir-06-2015-0208","relation":{},"ISSN":["1468-4527"],"issn-type":[{"type":"print","value":"1468-4527"}],"subject":[],"published":{"date-parts":[[2016,11,14]]}}}