Online Customer Review Platform Categorization to Identify Market Needs

Today’s globalized business environment has made business demands more dynamic and complex than ever. Accordingly, companies have confirmed that customer demands emanate from online customer review platforms, whose importance they have recognized as being important. There are many studies about extracting market needs from online customer review platforms. However, the research focuses on these processes and is limited to specific platforms. Limitations arise when diverse types of OCR platforms exist, due to processes being limited to a specific platform. Therefore, we categorized these platforms and tested the differences in their underlying market needs information with a quantitative approach. Part-of-speech tagging, named-entity recognition, and lexicon-based sentiment analysis were applied as methodologies. The academic contribution of this study is to suggest that an appropriate data source should be selected according to the market for extracting user review data. A practical contribution of this study is reduced waste of materials and human resources among companies through a basic stage of an optimized analysis process.