@article{Gohil:2018:10.2196/publichealth.5789,
author = {Gohil, S and Vuik, S and Darzi, A},
doi = {10.2196/publichealth.5789},
journal = {JMIR Public Health and Surveillance},
pages = {e43--e43},
title = {Sentiment analysis of health care tweets: review of the methods used.},
url = {http://dx.doi.org/10.2196/publichealth.5789},
volume = {4},
year = {2018}
}

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TY  - JOUR
AB  - BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the
AU  - Gohil,S
AU  - Vuik,S
AU  - Darzi,A
DO  - 10.2196/publichealth.5789
EP  - 43
PY  - 2018///
SN  - 2369-2960
SP  - 43
TI  - Sentiment analysis of health care tweets: review of the methods used.
T2  - JMIR Public Health and Surveillance
UR  - http://dx.doi.org/10.2196/publichealth.5789
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29685871
UR  - http://hdl.handle.net/10044/1/59674
VL  - 4
ER  - 

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