Detection of Opinion Spam with Character n-grams

In this paper we consider the detection of opinion spam as a stylistic classification task because, given a particular domain, the deceptive and truthful opinions are similar in content but differ in the way opinions are written (style). Particularly, we propose using character n-grams as features since they have shown to capture lexical content as well as stylistic information. We evaluated our approach on a standard corpus composed of 1600 hotel reviews, considering positive and negative reviews. We compared the results obtained with character n-grams against the ones with word n-grams. Moreover, we evaluated the effectiveness of character n-grams decreasing the training set size in order to simulate real training conditions. The results obtained show that character n-grams are good features for the detection of opinion spam; they seem to be able to capture better than word n-grams the content of deceptive opinions and the writing style of the deceiver. In particular, results show an improvement of 2.3% and 2.1% over the word-based representations in the detection of positive and negative deceptive opinions respectively. Furthermore, character n-grams allow to obtain a good performance also with a very small training corpus. Using only 25% of the training set, a Naïve Bayes classifier showed

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values up to 0.80 for both opinion polarities.