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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 10, 2019.
Abstract: The paper presents a computational method that mines, processes and analyzes Twitter data for detecting public sentiment of medicine. Self-reported patient data are collected over a period of three months by mining the Twitter feed, resulting in more than 10,000 tweets used in the study. Machine learning algorithms are used for an automatic classification of the public sentiment on selected drugs. Various learning models are compared in the study. This work demonstrates a practical case of utilizing social media in identifying customer opinions and building a drug effectiveness detection system. Our model has been validated on a tweet dataset with a precision of 70.7%. In addition, the study examines the correlation between patient symptoms and their choices for medication.
Daisuke Kuroshima and Tina Tian, “Detecting Public Sentiment of Medicine by Mining Twitter Data” International Journal of Advanced Computer Science and Applications(IJACSA), 10(10), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101001
@article{Kuroshima2019,
title = {Detecting Public Sentiment of Medicine by Mining Twitter Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101001},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101001},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {10},
author = {Daisuke Kuroshima and Tina Tian}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.