A linguistic variable of product-related question answering review system

Abstract

E-commerce platforms like Amazon, Jumia, Airbnb, Alibaba, eBay and JD.com amongst others play a huge role in linking sellers of products to interested buyers. Consumers generally ask questions in order to know if the product of interest will meet their needs. Relying on existing product reviews posted online by other consumers who have once purchased and used the product becomes a viable option to waiting for direct answers from the community question answer system. They are usually forced to spend time manually wading through these numerous customer reviews, a task which is cumbersome. This motivated to use Bi-directional Auto-regressive Transformer (BART) model for both question-answerability classification and answer generation. A dataset provided by Gupta et al. and conducted on Amazon Mechanical Turk was used for the experiment. It was observed that BART outperformed the heuristic baselines on all the metrics considered with existing models of long short term memory, convolutional neural network and bidirectional encoder representative from transformers. Based on the BART performance, a fuzzy based rule was introduced with system parameters defined for the classification of product-related question answering review into linguistic variables for good purchase decision making. The classification is in line with well-formed responses to product-related questions based on existing customer reviews using BART heuristic baselines.