Topic Modelling on Pharmaceutical Incident Data

Deepu Dileep, Soumya Rudraraju and V. V. HaraGopal
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Deepu Dileep: Aizant Global Analytics Pvt Ltd, India
Soumya Rudraraju: Aizant Global Analytics Pvt Ltd, India
V. V. HaraGopal: Aizant Global Analytics Pvt Ltd, India

European Journal of Mathematics and Statistics, 2021, vol. 2, issue 3, 92-96

Abstract: Focus of the current study is to explore and analyse textual data in the form of incidents in pharmaceutical industry using topic modelling. Topic modelling applied in the current study is based on Latent Dirichlet Allocation. The proposed model is applied on a corpus containing 190 incidents to retrieve key words with highest probability of occurrence. It is used to form informative topics related to incidents.

Keywords: Coherence Score; Incidents; Latent Dirichlet Allocation (LDA); Textual Mining; Topic Modelling (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejmath:v:2:y:2021:i:3:id:14033

DOI: 10.24018/ejmath.2021.2.3.33

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