Extracting Customer Reviews from Online Shopping and Its Perspective on Product Design

Author:

Anh Kieu Que1,Nagai Yukari1,Nguyen Le Minh2

Affiliation:

1. School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), Ashahidai 1-1, Nomi, Ishikawa, Japan

2. School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Ashahidai 1-1, Nomi, Ishikawa, Japan

Abstract

This paper presents a study on how we can extract helpful review from customers and its effect on the early phase when designing a product. We present a framework for analyzing the reviews from online shopping sites by detecting helpful reviews, aspects, and top reviews. We conduct a through analysis on review comments of the Amazon site and form a novel framework which can automatically extract useful information from review documents and it can also collaboratively work between designers and opinion customers. Experimental results on helpful review identification and sentiment classification showed that the proposed model achieved promising results. We also conduct an interview with designers to assert whether or not the proposed framework is effective. The results showed that the proposed framework is helpful for designers.

Publisher

World Scientific Pub Co Pte Lt

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1. Applying TRIZ and Kansei engineering to the eco-innovative product design towards waste recycling with latent Dirichlet allocation topic model analysis;Engineering Applications of Artificial Intelligence;2024-07

2. Identification of significant features and machine learning technique in predicting helpful reviews;PeerJ Computer Science;2024-01-23

3. Sentiment Analysis of the Amazon Customers Using the BiGRU Neural Network Enhanced by Attention Mechanism;2023 14th International Conference on Information and Knowledge Technology (IKT);2023-12-26

4. Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews;Engineering, Technology & Applied Science Research;2023-06-02

5. Multimodal Sentiment Analysis using Prediction-based Word Embeddings;2022 International Conference on Edge Computing and Applications (ICECAA);2022-10-13

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