Sentiment Analysis Tool for Pharmaceutical Industry & Healthcare
Keywords:Pharmaceutical industry, Sentiment Analysis, real-time analytics, healthcare, public mood, Social media.
Sentiment analysis (SA) is broadly used to analyze people’s opinions about a product or an event to identify breakpoints in public opinion. Particularly, pharmaceutical companies use SA to ensure they gain a competitive edge through better understanding of patients’ experiences allowing for more personalization and high responsiveness to consumers on social media. Patients self-reports on social media, frequently capture varied elements ranging from medical issues, product accessibility issues to potential side effects. The exploitation of such overwhelming unstructured data on social media through SA is of critical importance, however, the general-purpose sentiment analysis tools are not adapted and do not incorporate the specific lexicon used in the life sciences/pharma context which reduces the ability of such tools to accurately detect the meanings of the sentiments expressed towards treatments/scientific studies and pharma companies at large. Indeed, those tools involve generalized dictionaries, techniques and methods that face several challenges to detecting the suitable sentiment polarity. In this paper, we develop a dedicated research tool that extract in real time the sentiments and emotions conveyed by users on social media regarding pharmaceutical industries. Our focus is to improve the analyze of opinion about a target in pharmaceutical industries based on hybrid approaches used in single unified system throughout sentiment analysis process to seek the relevant sentiment polarity, which is tailored specifically to detect positive, negative or neutral opinions of patients and consumer health. This paper proposes a first architecture validated by a real and industry case.
(1) Ramon et al. “SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks”, Published by Elsevier Ireland, http://dx.doi.org/10.1016/j.ijmedinf.2015.09.007.
(2) Cieliebak et al. , “Potential and limitations of commercial sentiment detection tools”, Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013), A Workshop of the XIII International Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), Turin, Italy, December 3, 2013, 2013, pp. 47–58 http://ceur-ws.org/Vol-1096/paper4.pdf.
(3) Abbasi et al. “Benchmarking twitter sentiment analysis tools”,Proceedings of the Ninth International Conference on Language Resourcesand Evaluation (LREC-2014), Reykjavik, Iceland, May 26–31,2014,2014,pp.823–829http://www.lrec-.org/proceedings/lrec2014/ summaries/483.html.
(4) Turdakov et al.” aframework for text analysis”, Program. Comput. Software 40 (5) (2014)288–295,http://dx.doi.org/10.1134/s0361768814 050090.
(5) Pollyanna et al. “Comparing and Combining Sentiment
Analysis Methods”, not published.
(6) Montoyo et al., “ subjectifivty and sentiment analysis:: an overview of the current state of the area and envisaged developments”, decis support syst.53 (2012) 675–679.
(7) KANG et al. “ review-based measurement of customer satifaction in mobile service: Sentiement analysis and vikor approach, expert syst. APPL.(2013), HTTP://DX.DOI.ORG/10.1016/J.ESWA.2013.07.101
(8) KUHN et al., “A side effect resource to capture phenotypic effects of drugs”, MOL. SYST. BIOL. 6 (1) (2010) 343.
(9) BENTON et al., “Identifying potential adverse effects unsing the web: a new approach to medical hypothesis generation”, J. BIOMED. INFORM. 44(6) (2011) 989–996
(10) MAO et al. “ Online discussion of drug side effects and discontinuation among breast cancer survivors, Pharmaceopidemiol. DRUG SAF”.22 (3) (2013) 256–262.
(11) SARKER et al.” portable automatic text classification for adverse drug reaction detetction via multi-corpus training”, 53 (2015) 196–207.
(12) SEGURA-BEDMAR et al.,” Exploring spanish health social media for detecting drug effects”, BMC MED. INFORM. DECIS”.MAKING 15 (SUPPL. 2) (2015) S6
(13) CHEE et al., “Predicting adverse drug events from personal health messages”, AMIA ANNUAL SYMPOSIUM PROCEEDINGS”, 2011, AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2011, P. 217.
(14) WU et al.,”Exploiting online discussions to discover unrecognized drug side effects, methods inform”, MED. 52 (2) (2013) 152–159.
(15) Nikfarjam, A., & Gonzalez, G. H. (2011). Pattern mining for extraction of mentions of adverse drug reactions from user comments. In AMIA Annual Symposium Proceedings (Vol. 2011, p. 1019). American Medical Informatics Association.
(16) K. PORTIER, G.E. GREER, L. ROKACH, N. OFEK, Y. WANG,
P. BIYANI, M. YU, S. BANERJEE, K. ZHAO, P. MITRA, J. YEN,” understaning topics and sentiment in an online cancer survivor community “, J. NATL. CANCER INST.—MONOGR. 47(2013) 195–198.
(17) F. Liu, L.D. Antieau, H. Yu, Toward automated consumer question answering:automatically separating consumer questions from professional questions inthe healthcare domain, J. Biomed. Inform. 44 (6) (2011) 1032–1038
(18) M. ABDUL-MAGEED, M. DIAB, S. KÜBLER, SAMAR, “Subjectivity and sentiment analysis for arabic social media”,COMPUT. SPEECH LANG. 28 (2014) 20–37
(19) Asghar MZ1, Ahmad S2, Qasim M1, Zahra SR1, Kundi FM1.SentiHealth: creating health-related sentiment lexicon using hybrid approach. 10.1186/s40064-016-2809-x 2016.
(20) R. Leaman, L. Wojtulewicz, R. Sullivan, A. Skariah, J. Yang, G. Gonzalez, Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks, in: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Association for Computational Linguistics, 2010, pp. 117–125.