Warranty operation enhancement through social media knowledge: a deep-learning methods

Abstract

People use social media as free channels to share their sentiments and experiences during all stages of consuming a product or service. Likewise, corporations depend on social media as feedback sources that influence the positioning of their products/services in the market. This paper aims to recognise the frequent product flaws and warranty issues through social network mining. We have performed ontology-based methods, text mining, and sentiment analysis using deep learning methods on social media data to investigate product failures, symptoms, and the correlation between warranty programs and customer behaviour. Correspondingly, a multi-sources mining approach has been incorporated into social media mining to cover all the occasions. Furthermore, we promoted a decision support system to learn practically through customer feedback. Finally, to validate the accuracy and reliability of the results, we used the claimed data of the laptop industry to compare our derivatives and machine learning validation metrics to ensure accuracy.