| dc.description.abstract | Nowadays, people got accustomed to participate interaction frequently on the web in the era of internet. They were not only sharing their real-life experience actively , but also playing the role of the recipient of the message, and thus generated a lot of comments had become references for most people buying products or services. The survey data also showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. However, individual consumers and commercial organizations would cause damage by abusing online reviews. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. These comments recorded of useful tourist information and personal experience which were important information for travelers in unfamiliar places before departing.
This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words、personal pronoun、negative words、pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well. After integrating classifiers, the four indicators for “ Precision”, ”Recall”, “Accuracy”, and “F-measure” had better efficacy than single classifier , also could avoid the risk of unsuitable for other data set. | en_US |