Extracting Sentiments and Summarizing Health Reviews from Social Media Using Machine Learning Techniques
DOI:
https://doi.org/10.14738/tmlai.61.3595Keywords:
National and International repute, Medical Reviews, Health Consumer, Summarization of text and health featuresAbstract
Most of the health organizations provide an array of medical services and request their beneficiaries to provide their experience’s in the form of opinion/reviews for which they are associated. Doctors of national and international repute have hundreds and even thousands of reviews authored by the health consumers around the globe. For an individual it is difficult and time consuming process to look all the reviews before taking an appropriate decision. Thus it is necessary to summarize the reviews to make an individual to take prompt decision. For a doctor it is also difficult to keep track of patient’s reviews given by the patients in different time intervals, but he may have the summary of his entire patient’s reviews to understand what is the best can be done to the patient’s community. This research paper aims to mine and summarize the medical reviews authored by the health consumers. This article is performed by summarization of text in three steps, the first step is to identify the health features that have been commented by health consumers, the next one is to identify opinions of each review sentence and deciding whether each opinion sentence is positive or negative and finally summarizing the results.
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