FAHPBEP: A Fuzzy Analytic Hierarchy Process Framework in Text Classification

With the availability of websites and the growth of comments, reviews of user-generated content are published on the Internet. Sentiment Classification is one of the most common problems in text mining, which applies to categorize reviews into positive and negative classes. Pre-processing has an important role when these textual contexts are employed by machine learning techniques. Without efficient pre-processing methods, unreliable results will be achieved. This research probes to investigate the performance of pre-processing for the Sentiment Classification problem on three popular datasets. We suggest a high-performance framework to enhance classification performance. First, features of user's opinions are extracted based on three methods: (1) Backward Feature Selection; (2) High Correlation Filter; and (3) Low Variance Filter. Second, the error rate of the primary classification for each method is calculated through the perceptron. Finally, the best method is selected through the fuzzy analytic hierarchy process. This framework is beneficial for companies to observe people's comments about their brands and for many other applications. The current authors have provided further evidence to confirm the superiority of the proposed framework. The obtained results indicate that on average this proposed framework outperformed its counterparts. This framework yields 90.63 precision, 90.89 accuracy, 91.27 recall, and 91.05% f-measure.