SPAR: A system to detect spam in Arabic opinions
Heider A. Wahsheh, Mohammed Naji Al-Kabi, Izzat Mahmoud Alsmadi · 2013
The evaluation of the public opinion through websites, social networks, news feedback, etc. is currently getting an extensive research to discover public opinion regarding the current social and political changes in the Middle Eastern countries. However, the level of trust or confidentiality of such public opinion evaluations may have the risk of being spammed. This study aims to detect the spam opinions in the Yahoo!-Maktoob social network. The proposed system reads the opinions and classifies them into one of the following two classes: spam and non-spam opinions, based on a number of features. Each spam opinion categorizes into; high levels spam and low level spam, based on special metrics. While each non-spam opinion is labeled as; positive, negative, or neutral based on the language polarity dictionaries. Those dictionaries include words that can be classified as: positive, negative or neutral. The proposed system adopts machine learning classification technique to perform classification and prediction.