TY - JOUR
T1 - A Comparative Study of Text Classifier for Mobile Crowdsensing Applications
AU - Rajoo, Sharmiladevi
AU - Magalingam, Pritheega
AU - Idris, Norbik Bashah
AU - Narayana Samy, Ganthan
AU - Maarop, Nurazean
AU - Shanmugam, Bharanidharan
AU - Perumal, Sundaresan
PY - 2018/1
Y1 - 2018/1
N2 - Mobile reporting applications are useful mainly for reporting real-time issues related to public infrastructure, environmental or social incidents through smart mobile devices. The credibility of the cases reported are often a great challenge because users may report false information and as a result this affects the response team in the aspect of time, energy and other resources. Researchers in the past have developed many report trust estimation algorithms that focuses on user’s location, behavior and reputation. We aim to analyze the textual part of a report. Text analyses have been used for email spam filtering and sentiment analysis but have not been used for false report identification. Therefore, the purpose of this study is to compare different text classification algorithms and propose a suitable classifier for distinguishing the genuine and fake reports. The comparative analysis can be used by other researchers in the area of false report or fake message identification.
AB - Mobile reporting applications are useful mainly for reporting real-time issues related to public infrastructure, environmental or social incidents through smart mobile devices. The credibility of the cases reported are often a great challenge because users may report false information and as a result this affects the response team in the aspect of time, energy and other resources. Researchers in the past have developed many report trust estimation algorithms that focuses on user’s location, behavior and reputation. We aim to analyze the textual part of a report. Text analyses have been used for email spam filtering and sentiment analysis but have not been used for false report identification. Therefore, the purpose of this study is to compare different text classification algorithms and propose a suitable classifier for distinguishing the genuine and fake reports. The comparative analysis can be used by other researchers in the area of false report or fake message identification.
KW - mobile crowdsensing
KW - false report
KW - text classification
KW - classifier
U2 - 10.1166/asl.2018.11788
DO - 10.1166/asl.2018.11788
M3 - Article
SN - 1936-6612
VL - 24
SP - 686
EP - 689
JO - Advanced Science Letters
JF - Advanced Science Letters
IS - 1
ER -