Abstract
The outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 457-466 |
| Number of pages | 10 |
| Journal | Proceedings of the Design Society |
| Volume | 1 |
| Early online date | Jul 27 2021 |
| DOIs | |
| State | Published - Aug 2021 |
| Event | 23rd International Conference on Engineering Design, ICED 2021 - Gothenburg, Sweden Duration: Aug 16 2021 → Aug 20 2021 |
Keywords
- Machine learning
- Market implications
- Semantic data processing
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Software
- Modeling and Simulation