NORMA eResearch @NCI Library

Sentiment Analysis of Medicine Reviews using Ensemble models

Avverahalli Ramesha, Prashanth (2017) Sentiment Analysis of Medicine Reviews using Ensemble models. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (1MB) | Preview


The need to analyze user generated data over the web has recently gained importance due to the abundance of knowledge which can be acquired by careful analysis of such data. Majority of such data is available via online networking websites like Facebook, Twitter, LinkedIn, etc. The data available in such platforms are in the form of opinions and reviews of products, movies, medications, hotels, etc. Mining and analyzing of such data has become an important aspect for the companies to understand the people's opinion on a particular subject. There has been enough research done in the application of sentiment analysis across domains like product reviews, movies, hotels, etc. However, utilization of such methodologies in the field of medicine has to be given more importance as there are several studies conducted by United States Food and Drug Administration on the effects of adverse drug reactions on patients. Studying the effects of commonly used drugs on patients is important for the pharmaceutical companies to understand the positive and negative effect of drugs on the patients. The motive of this research project is to apply machine learning models for the sentiment analysis of reviews posted by patients to determine the polarity of opinion expressed in the reviews which can be positive or negative.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > Healthcare Industry
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Caoimhe Ni Mhaicin
Date Deposited: 28 Aug 2018 16:15
Last Modified: 28 Aug 2018 16:15

Actions (login required)

View Item View Item