Title :
Classification of advertising spam reviews
Author :
Park, Insuk ; Kang, Hanhoon ; Lee, Chang Yeol ; Yoo, Seong Joon
Author_Institution :
Dept. of Comput. Eng., Sejong Univ., Seoul, South Korea
fDate :
Nov. 30 2010-Dec. 2 2010
Abstract :
In this study, methods to extract advertising reviews from shopping mall reviews are suggested. Advertising reviews are mostly written by companies and contain advertising contents. There are a few studies regarding the classification of opinion spam documents, which is very rare in foreign studies; however, there are no studies that classify advertising reviews from Korean reviews. In this study, the Naïve Bayes Classifier was used to classify advertising reviews. POS-Tag+Bigram, POS-Tagging+ Unigram, and Bigram were used to extract specific words that are used for probability calculation. When the POS-Tagging+Bigram method was used, the f-measure of advertising reviews was the most exact at 83.1%.
Keywords :
Bayes methods; advertising data processing; pattern classification; unsolicited e-mail; Naïve Bayes classifier; POS-Tag+Bigram; POS-Tagging+ Unigram; advertising spam reviews classification; shopping mall reviews; spam documents; Advertising; Feature extraction; Probability; Tagging; Training; Unsolicited electronic mail; Advertising Review; Opinion Review; Spam Review;
Conference_Titel :
Advanced Information Management and Service (IMS), 2010 6th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-8599-4
Electronic_ISBN :
978-89-88678-32-9
