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Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study

According to the previous study by Aramaki et al [15], most people report influenza information precisely in the early stage of an influenza season.

Shoko Wakamiya, Yukiko Kawai, Eiji Aramaki

JMIR Public Health Surveill 2018;4(3):e65


Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Denecke et al [29] collected data from multiple media sites with keyword lists and classified texts as relevant/irrelevant using support vector machines.Although no previous studies have been completed in Japanese, Aramaki et al [30] reported on a system to

Misa Usui, Eiji Aramaki, Tomohide Iwao, Shoko Wakamiya, Tohru Sakamoto, Mayumi Mochizuki

JMIR Med Inform 2018;6(3):e11021


Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis

Aramaki et al discovered that a Twitter-based model outperformed a Google-based model during periods of normal news coverage, although the Twitter model performed less optimally during the periods of excessive media coverage [23].

J Danielle Sharpe, Richard S Hopkins, Robert L Cook, Catherine W Striley

JMIR Public Health Surveill 2016;2(2):e161