Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

Abstract

Opinions in reviews about the quality of products or services can be important information for readers. Unfortunately, such opinions may include deceptive ones posted for some business reasons. To keep the opinions as a valuable and trusted source of information, we propose an approach to detecting deceptive and truthful opinions. Specifically, we explore the use of word and character n-gram combinations, function words, and word syntactic n-grams (word sn-grams) as features for classifiers to deal with this task. We also consider applying word correction to our utilized dataset. Our experiments show that classification results of using the word and character n-gram combination features could perform better than those of employing other features. Although the experiments indicate that applying the word correction might be insignificant, we note that the deceptive opinions tend to have a smaller number of error words than the truthful ones. To examine robustness of our features, we then perform cross-classification tests. Our latter experiments results suggest that using the word and character n-gram combination features could work well in detecting deceptive and truthful opinions. Interestingly, the latter experimental results also indicate that using the word sn-grams as combination features could give good performance.