OICC Press Majlesi Journal of Electrical Engineering 2345-3796 14 3 2024 02 15 FAHPBEP: A Fuzzy Analytic Hierarchy Process Framework in Text Classification 10.29252/mjee.14.3.14 EN Razieh Asgarnezhad Department of Computer Engineering, Aghigh Institute of Higher Education Shahinshahr, 8314678755, Isfahan, Iran Sayed Monadjemi Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran. MohammadReza Soltanaghaei Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. Journal Article 2024 02 15 With the availability of websites and the growth of comments, reviews of user-generated content 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 employed by machine learning techniques. Without efficient pre-processing methods, unreliable results will achieve. 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 calculated through the perceptron. Finally, the best method 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.