DATA MINING APPROACH FOR CUSTOMER REVIEW BASED PRODUCT RANKING IN ONLINE PRODUCT RECOMMENDATION

Abstract

Product recommendation systems use ratings from users to rank products. There’s a gap between analyzing user ratings and reviews in purchase decision making process. 97% people read reviews to confirm product quality and trust reviews than ratings. This research proposes a data mining and machine learning model to rank products based on textual reviews. When considering methodology and design of this study, a survey was conducted and outcomes show a needfulness of using reviews in ranking. A model to rank products considering review sentiment polarity is proposed and implemented using Python programming language. Well-structured unique workflow of data pre-processing, sentiment-polarity estimation, algorithm training for high accuracy, best algorithm selection, value prediction and calculation for ranking products for recommendation is used. As the results, Survey indicates that 98.8% of people read reviews though the star rating is presented. 85.8% say they trust this kind of system more. Among four algorithms, K-Neighbors algorithm was proposed as best performing algorithm for value prediction for this type of research. Products were successfully ranked based on sentiment score. Most of existing researches are proposal researches while this is an implementation research. Proposed algorithm and model with high accuracy can use as base for future researches. Illustrated Python implementation method also can be used for future work. There are some practical implications as Fake review generation can mislead the outcomes and reviews from other languages, rather than English, will not be considered for calculations and have to train and create lexical database for other languages.