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Journal of Pharmacognosy and Phytochemistry

Journal of Pharmacognosy and Phytochemistry

Vol. 7, Special Issue 1 (2018)

Social media (Twitter) Data analysis using maximum entropy classifier on big data processing framework (Case study: Analysis of health condition, education status, states of business)

Author(s):

Hein Htet and Yi Yi Myint

Abstract:
Most of the people aren’t aware about their health situation, and they don’t interest which level has been stand by their nation in case of business and health states. These factors are considered as important things to be improved each nation. Therefore, it is needed to focus these things not only citizens but also the authorities of each country. But, it can be difficult to focus these stated factors without using the modern computer technology. Nowadays, most of the people friendly used social media and people have started expressing their feelings and activities on it. And so, social media is a valuable source to analyze these things by using data mining techniques. Therefore, social media (Twitter) data analysis system is developed to know about health condition, education status, and states of business which are good, fair, or bad based on the data that they post on the Twitter. Maximum Entropy classifier is used to perform sentiment analysis on their tweets to suggest these stated conditions. It is interacting with Twitter data (big data environment), and so, big data processing framework is built to efficiently handle large amount of Twitter data

Pages: 695-700  |  1976 Views  657 Downloads

How to cite this article:
Hein Htet and Yi Yi Myint. Social media (Twitter) Data analysis using maximum entropy classifier on big data processing framework (Case study: Analysis of health condition, education status, states of business). J Pharmacogn Phytochem 2018;7(1S):695-700.

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