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Proceedings ArticleDOI

Summarization and sentiment analysis from user health posts

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TLDR
This work collects real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them, and proposes to classify the users based on their `emotional state of mind'.
Abstract
Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this work we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining.

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References
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Association rule mining to detect factors which contribute to heart disease in males and females

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Journal ArticleDOI

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Proceedings ArticleDOI

People on drugs: credibility of user statements in health communities

TL;DR: The authors proposed a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources, which can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
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