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ABSTRACT:
Social media has become a major
source for analyzing all aspects of daily life. Thanks to dedicated latent
topic analysis methods such as the Ailment Topic Aspect Model (ATAM), public
health can now be observed on Twitter. In this work, we are interested in using
social media to monitor people’s health over time. The use of tweets has
several benefits including instantaneous data availability at virtually no
cost. Early monitoring of health data is complementary to post-factum studies
and enables a range of applications such as measuring behavioral risk factors
and triggering health campaigns. We formulate two problems: health transition
detection and health transition prediction. We first propose the Temporal
Ailment Topic Aspect Model (TM–ATAM), a new latent model dedicated to solving
the first problem by capturing transitions that involve health-related topics.
TM–ATAM is a non-obvious extension to ATAM that was designed to extract
health-related topics. It learns health-related topic transitions by minimizing
the prediction error on topic distributions between consecutive posts at
different time and geographic granularities. To solve the second problem, we
develop T–ATAM, a Temporal Ailment Topic Aspect Model where time is treated as
a random variable natively inside ATAM. Our experiments on an 8-month corpus of
tweets show that TM–ATAM outperforms TM–LDA in estimating health-related
transitions from tweets for different geographic populations. We examine the
ability of TM–ATAM to detect transitions due to climate conditions in different
geographic regions. We then show how T–ATAM can be used to predict the most
important transition and additionally compare T–ATAM with CDC (Center for
Disease Control) data and Google Flu Trends.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Sumit Sidana, Sihem Amer-Yahia,
Marianne Clausel, Majdeddine Rebai, Son T. Mai, Massih-Reza Amini, “Health
Monitoring on Social Media over Time”, IEEE Transactions on Knowledge and Data
Engineering, 2018