Original Article

Adverse Drug Reaction Related Post Detecting Using Sentiment Feature



Background: The posts related to Adverse Drug Reaction (ADR) on social websites are believed to be valuable resource for post-marketing drug surveillance. Beyond domain feature, the aim of this study was to find a more effective method to detect ADR related post.

Methods: We conducted experiment on posts using sentiment features from March 8 to May 20 in 2016 in Shanghai of China. Firstly, the diabetes posts were collected; the 1814 posts were annotated by hand. Secondly, sentiment features set were generated and the (CHI) statistics were used to select feature. Finally, we evaluated the effectiveness of our method using the different feature sets.

Results: By comparing the posts detection performance of different feature sets, using sentiment features by CHI statistics can improve ADR related post detection performance. By comparing the ADR-related group with the non-ADR group, performance of ADR related post detection was better than the performance of non-ADR post detection. We could obtain highest performance owing to introducing sentiment feature and using CHI feature selection technique, and the method was proved to be effective during detecting post related to ADR.

Conclusion: By using sentiment feature and CHI feature selection technique, we can get an effective method to detect post related to ADR.


Harpaz R, DuMouchel W, Shah H et al (2012). Novel Data-Mining Methodolo-gies for Adverse Drug Event Discovery and Analysis. Clin Pharmacol Ther, 91(6): 1010-21.

Hesse W, Hansen D, Finholt T et al (2010). Social participation in health 2.0. Computer (Long Beach Calif), 43(11): 45-52.

Edwards R, Lindquist M (2011). Social me-dia and networks in pharmacovigilance. Drug Saf, 34(4): 267-271.

Bian J, Topaloglu U, Yu F (2012). Towards large-scale twitter mining for drug-related adverse events. SHB12 (2012), 2012: 25–32..

Yates A, Goharian N (2013). ADRTrace: de-tecting expected and unexpected adverse drug reactions from user reviews on so-cial media sites. In: Serdyukov P. et al. (eds) Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg.

White W, Tatonetti P, Shah H et al (2013). Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Inform Assoc, 20(3):404-408.

Chou S, Hunt M, Beckjord B et al (2009). Social media use in the United States: im-plications for health communication. J Med Internet Res, 11(4):e48.

Freifeld C, Brownstein S, Menone M et al (2014). Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf, 37(5): 343-350.

Leaman R, Wojtulewicz L, Sullivan R et al (2010). Towards internet-age pharma-covigilance: extracting adverse drug reac-tions from user posts to health-related social networks. In Proceedings of the 2010 workshop on biomedical natural language processing.

Jiang K, Zheng Y. (2013) Mining Twitter Data for Potential Drug Effects. In: Mo-toda H, Wu Z, Cao L et al (eds) Ad-vanced Data Mining and Applications. ADMA 2013. Lecture Notes in Comput-er Science, vol 8346. Springer, Berlin, Heidelberg.

Tuarob S, Tucker S, Salathe M, Ram N (2014). An ensemble heterogeneous clas-sification methodology for discovering health-related knowledge in social media messages. J Biomed Inform, 49: 255-268.

Benton A, Ungar L, Hill S et al (2011). Iden-tifying potential adverse effects using the web: A new approach to medical hypoth-esis generation. J Biomed Inform, 44(6), 989-996.

Yeleswarapu S, Rao A, Joseph T et al (2014). A pipeline to extract drug-adverse event pairs from multiple data sources. BMC Med Inform Decis Mak, 14: 13.

Gurulingappa H, Rajput M, Roberts A et al (2012). Development of a benchmark corpus to support the automatic extrac-tion of drug-related adverse effects from medical case reports. J Biomed Inform, 45(5): 885-892.

Basch EM, Thaler HT, Shi W et al (2004). Use of information resources by patients with cancer and their companions. Cancer, 100(11): 2476-83.

Kuhn M, Campillos M, Letunic I et al (2010). A side effect resource to capture pheno-typic effects of drugs. Mol Syst Biol, 6: 343.

Zeng-Treitler Q, Goryachev S, Tse T et al (2008). Estimating consumer familiarity with health terminology: a context-based approach. J Am Med Inform Assoc, 15(3): 349-356.

Qiu B, Zhao K, Mitra P et al (2011). Get online support, feel better-sentiment analysis and dynamics in an online cancer survivor community. In Privacy, Security, Risk and Trust and 2011 IEEE Third Inernational Conference on Social Com-puting.

Thelwall M, Buckley K, Paltoglou G et al (2010). Sentiment strength detection in short informal text. J Am Soc Inf Sci Tech-nol, 61(12): 2544-2558.

Baccianella S, Esuli A, Sebastiani F (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. Proceedings of the 7th International Language Resources and Evaluation Conference.

Satorra A, Bentler M (2010). Ensuring posi-tiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2): 243-248.

Yang CC, Yang H, Jiang L, Zhang M (2012). Social media mining for drug safety signal detection. SHB '12 Proceedings of the 2012 international workshop on Smart health and wellbeing. Pages 33-40.

IssueVol 47 No 6 (2018) QRcode
SectionOriginal Article(s)
Adverse drug reaction Post Sentiment feature

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
LIU J, JIANG X, CHEN Q, SONG M, LI J. Adverse Drug Reaction Related Post Detecting Using Sentiment Feature. Iran J Public Health. 2018;47(6):861-867.