Extraction of Product Defects and Opinions from Customer Reviews by Using Text Clustering and Sentiment Analysis
Mustafa Çataltaş, Sevcan Doğramacı, Semih Yumuşak, Kasım Öztoprak · 2020
The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.