Constructing an Integrated Classifiers for Identifying Authenticity and Sentiment Analysis- A Case of Hotel Reviews

Most of the consumers will search for the reviews of products or service to help them make a decision to shopping or not, so they consider the authenticity of comments is very important. Moreover, company will collect positive and negative comments to realize their position within the market, and sentiment analysis is regarded as a beneficial tool. Most of the researches only focused on Identifying authenticity or sentiment analysis, our study attempted to solve both of two issues. We constructed an integrated classifier based on text mining approaches for Identifying authenticity and sentiment analysis, the data which this study used was collected by Amazon Mass Intelligence Platform and Trip Advisor hotel review website. There are two experiments tested in this study which are mixed judgment and judge by step. The mixed judgment was analyzing real vs. fake and positive vs. negative reviews into a matrix. Additionally, judge by step was analyzing the reality of reviews then conducting sentiment analysis. With the experimental results, the system will figure out the keywords through two theories--sentiment analysis and rumor/lie theory, based on sentiment analysis the comments could divide into positive and negative. Furthermore, if the user would like to know the comments are fake or not, then based on the rumor/lie theory. During the process, the experiments were not only use a single classifier in order to pick the best model but use the method of integrating to enhance the accuracy of classifier. The results of this study illustrated the comparison between judge by step and mixed judgment. The accuracy which in step method (77.29%) is better than mixed judgment (68.13%). This study found out the step methods is more complicate than mixed judgement, but the accuracy and F- measure are the better.