The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	A fuzzy logic-based text classification method for social media
Author:	Wu, Keyuan
View Online:	njit-etd2017-082 (xi, 58 pages ~ 1.4 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Master of Science
Program:	Electrical Engineering
Document Type:	Thesis
Advisory Committee:	Zhou, MengChu (Committee chair) Abdi, Ali (Committee member) Hu, Hesuan (Committee member)
Date:	2017-05
Keywords:	Social media Text classification Fuzzy logic
Availability:	Unrestricted
Abstract:	Social media offer abundant information for studying people’s behaviors, emotions and opinions during the evolution of various rare events such as natural disasters. It is useful to analyze the correlation between social media and human-affected events. This study uses Hurricane Sandy 2012 related Twitter text data to conduct information extraction and text classification. Considering that the original data contains different topics, we need to find the data related to Hurricane Sandy. A fuzzy logic-based approach is introduced to solve the problem of text classification. Inputs used in the proposed fuzzy logic-based model are multiple useful features extracted from each Twitter’s message. The output is its degree of relevance for each message to Sandy. A number of fuzzy rules are designed and different defuzzification methods are combined in order to obtain desired classification results. This work compares the proposed method with the well-known keyword search method in terms of correctness rate and quantity. The result shows that the proposed fuzzy logic-based approach is more suitable to classify Twitter messages than keyword word method.