TY - GEN
T1 - Predicting political mood tendencies based on Twitter data
AU - Hernandez-Suarez, A.
AU - Sanchez-Perez, G.
AU - Martinez-Hernandez, V.
AU - Perez-Meana, H.
AU - Toscano-Medina, K.
AU - Nakano, M.
AU - Sanchez, V.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/26
Y1 - 2017/5/26
N2 - Online social media has changed the way of interacting among users, nowadays, is used as a tool for expressing polarized opinions related to a global or specific context. Valuable information can be gathered in real-time basis and can help to determine if such data has a social impact on users represented as comfort or discomfort on a political domain. Analyzing data related to political domains like government, elections, security & defense and health insurance are important for measuring social mood and predicting whether there is a positive or negative tendency on selected populations. This paper presents a mood analysis methodology on Twitter data to predict social sentiment on political events. The proposed methodology is done by gathering streams of Twitter's information, then converted into trained data for processing and classification such that we can statistically predict if there is a positive or negative tendency on political events.
AB - Online social media has changed the way of interacting among users, nowadays, is used as a tool for expressing polarized opinions related to a global or specific context. Valuable information can be gathered in real-time basis and can help to determine if such data has a social impact on users represented as comfort or discomfort on a political domain. Analyzing data related to political domains like government, elections, security & defense and health insurance are important for measuring social mood and predicting whether there is a positive or negative tendency on selected populations. This paper presents a mood analysis methodology on Twitter data to predict social sentiment on political events. The proposed methodology is done by gathering streams of Twitter's information, then converted into trained data for processing and classification such that we can statistically predict if there is a positive or negative tendency on political events.
KW - Big Data
KW - Data
KW - Forecasting
KW - Nave Bayes
KW - Prediction
KW - Regularized Regression
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85021795992&partnerID=8YFLogxK
U2 - 10.1109/IWBF.2017.7935106
DO - 10.1109/IWBF.2017.7935106
M3 - Contribución a la conferencia
AN - SCOPUS:85021795992
T3 - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
BT - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Biometrics and Forensics, IWBF 2017
Y2 - 4 April 2017 through 5 April 2017
ER -