Ravali Mamidi 1 , Michele Miller 2 , Tanvi Banerjee 1,3 , William Romine 2 , Amit Sheth 1,3 Show Affiliations »
Abstract
BACKGROUND: To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies' efforts to disseminate relevant information to the public about Zika-related issues. OBJECTIVE: The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. METHODS: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. RESULTS: A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. CONCLUSIONS: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research. ©Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 04.06.2019.
BACKGROUND: To understand the public sentiment regarding the Zika virus , social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies' efforts to disseminate relevant information to the public about Zika-related issues. OBJECTIVE: The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed. METHODS: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category. RESULTS: A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category. CONCLUSIONS: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects ,Zika abnormalities , and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research. ©Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 04.06.2019.
Entities: Chemical
Disease
Gene
Species
Keywords:
Zika; epidemiology; infodemiology; infoveillance; machine learning; natural language processing; sentiment analysis; social media; twitter
Year: 2019
PMID： 31165711 PMCID： PMC6682293 DOI： 10.2196/11036
Source DB: PubMed Journal: JMIR Public Health Surveill ISSN： 2369-2960