TY - GEN
T1 - Sentiment Analysis of Monkeypox Tweets in Latin America
AU - Chire-Saire, Josimar
AU - Pineda-Briseño, Anabel
AU - Oblitas-Cruz, Jimy
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Even during the Covid-19 pandemic, a new outbreak of Monkeypox was confirmed in the United Kingdom, and from there it has spread throughout the world. Among the most affected countries are those located in Latin America, one of the most vulnerable regions due to the lack of adequate hospital infrastructure, the shortcomings or deficiencies of health care systems, or populations with a high prevalence of chronic diseases. Social media platforms, such as Twitter, have become valuable sources of real-time information and public sentiment during disease outbreaks. This study presents an analysis of sentiment expressed in tweets related to Monkeypox in Latin America, aiming to understand public perceptions and emotional responses. We collected a dataset of tweets containing keywords associated with Monkeypox, originating from Latin American countries, over a specific time frame (from May 31, 2022, to October 31, 2022). Natural language processing techniques were employed to preprocess and analyze the textual content. Sentiment analysis tools were applied to classify tweets into positive or negative sentiments. Our findings reveal valuable insights into the sentiment dynamics surrounding Monkeypox in Latin America. We observed fluctuations in sentiment over time, closely aligned with the progression of the disease and related events.
AB - Even during the Covid-19 pandemic, a new outbreak of Monkeypox was confirmed in the United Kingdom, and from there it has spread throughout the world. Among the most affected countries are those located in Latin America, one of the most vulnerable regions due to the lack of adequate hospital infrastructure, the shortcomings or deficiencies of health care systems, or populations with a high prevalence of chronic diseases. Social media platforms, such as Twitter, have become valuable sources of real-time information and public sentiment during disease outbreaks. This study presents an analysis of sentiment expressed in tweets related to Monkeypox in Latin America, aiming to understand public perceptions and emotional responses. We collected a dataset of tweets containing keywords associated with Monkeypox, originating from Latin American countries, over a specific time frame (from May 31, 2022, to October 31, 2022). Natural language processing techniques were employed to preprocess and analyze the textual content. Sentiment analysis tools were applied to classify tweets into positive or negative sentiments. Our findings reveal valuable insights into the sentiment dynamics surrounding Monkeypox in Latin America. We observed fluctuations in sentiment over time, closely aligned with the progression of the disease and related events.
KW - Machine Learning
KW - Monkeypox
KW - Sentiment Analysis
KW - Social Media
UR - http://www.scopus.com/inward/record.url?scp=85188662305&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-55486-5_17
DO - 10.1007/978-3-031-55486-5_17
M3 - Conference contribution
AN - SCOPUS:85188662305
SN - 9783031554858
T3 - Communications in Computer and Information Science
SP - 230
EP - 245
BT - Applied Machine Learning and Data Analytics - 6th International Conference, AMLDA 2023, Revised Selected Papers
A2 - Jabbar, M.A.
A2 - Tiwari, Sanju
A2 - Ortiz-Rodríguez, Fernando
A2 - Groppe, Sven
A2 - Bano Rehman, Tasneem
T2 - 6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023
Y2 - 9 November 2023 through 10 November 2023
ER -