TY - JOUR
T1 - A novel fuzzy logic-based text classification method for tracking rare events on twitter
AU - Lu, Xiaoyu Sean
AU - Zhou, Mengchu
AU - Wu, Keyuan
N1 - Funding Information:
Manuscript received April 9, 2019; accepted July 17, 2019. Date of publication August 22, 2019; date of current version June 16, 2021. This work was supported in part by Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) under Grant 119/2014/A3. This article was recommended by Associate Editor H. R. Karimi. (Corresponding author: MengChu Zhou.) X. S. Lu and K. Wu are with the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: xl267@njit.edu; kw242@njit.edu).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - A rare event, such as a natural disaster, does not occur frequently, but may cause catastrophic impacts on human beings and their living environment once it happens. Social media provides people an immediate and convenient way to share their opinions. Researchers can thus use it for investigating people's actions, feelings, and attitudes during multiple rare events. By using social media data, many studies try to build the connection between rare events in a real world and people's responses, including feelings, attitudes, and behaviors, in a virtual world. In this article, we propose a feature extraction and text classification approach to Twitter text data related to a rare event, e.g., Hurricane Sandy. First, a novel feature extraction method is proposed to mine useful features from each text message. Next, a fuzzy logic-based classification method is proposed to distinguish event-related and unrelated messages. Finally, our proposed method is compared with the existing keyword search one that is widely used in analyzing the evolution of a rare event. The result reveals that the proposed approach is suitable to identify the rare event-related and unrelated text messages.
AB - A rare event, such as a natural disaster, does not occur frequently, but may cause catastrophic impacts on human beings and their living environment once it happens. Social media provides people an immediate and convenient way to share their opinions. Researchers can thus use it for investigating people's actions, feelings, and attitudes during multiple rare events. By using social media data, many studies try to build the connection between rare events in a real world and people's responses, including feelings, attitudes, and behaviors, in a virtual world. In this article, we propose a feature extraction and text classification approach to Twitter text data related to a rare event, e.g., Hurricane Sandy. First, a novel feature extraction method is proposed to mine useful features from each text message. Next, a fuzzy logic-based classification method is proposed to distinguish event-related and unrelated messages. Finally, our proposed method is compared with the existing keyword search one that is widely used in analyzing the evolution of a rare event. The result reveals that the proposed approach is suitable to identify the rare event-related and unrelated text messages.
KW - Fuzzy logic
KW - social media
KW - text classification
KW - text feature extraction
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U2 - 10.1109/TSMC.2019.2932436
DO - 10.1109/TSMC.2019.2932436
M3 - Article
AN - SCOPUS:85112218358
SN - 2168-2216
VL - 51
SP - 4324
EP - 4333
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 7
M1 - 8809823
ER -