Sentiment Analysis Tool for Pharmaceutical Industry & Healthcare

  • Hanane Grissette LIIAN Laboratory Sidi Mohamed benAbdellah University Fez Morocco
  • EL Habib Nfaoui LIIAN Laboratory Sidi Mohamed ben Abdellah University Fez Morocco.
  • Adil Bahir IT Head/Business Information Manager, Novartis pharma Casablanca Morocco.
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.



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How to Cite
Grissette, H., Nfaoui, E. H., & Bahir, A. (2017). Sentiment Analysis Tool for Pharmaceutical Industry & Healthcare. Transactions on Machine Learning and Artificial Intelligence, 5(4).
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems