International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Clustered Probabilistic Aspect Summarization for Medical Reviews

Author : Devi Venugopal ¹ Remya R ²

Date of Publication :7th February 2016

Abstract: With the rising popularity of internet, online drug reviews have been proved to be extremely helpful for patients suffering from chronic diseases. Most of the patients search upon online reviews before taking any medicine. Online reviews, blogs, and discussion forums such as WebMD on chronic diseases and medicines are becoming important supporting resources for patients. Extracting useful information from these reviews is very difficult and challenging. Opinion mining or aspect mining involves the extraction of useful information (e.g. positive or negative sentiments of a product) from a large quantity of text opinions or reviews given by Internet users. Various algorithms had been proposed to extract information from the opinion of web users. Some of the algorithms are LDA, sLDA, NMF, SSNMF, DiscLDA and PAAM. Using clustering based probabilistic aspect summarization technique every user and medical experts can view the positive and negative aspects separately generated from large number of medical reviews. So common people can rate the medicines for chronic diseases which has high side effects.

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