Knowledge Learning of Replacement Judgment Using Word-of-mouth Data

Area of Science:

• Business and Marketing
• Consumer Behavior
• Technology Adoption

Background:

• The COVID-19 pandemic disrupted the camera industry, leading to event cancellations.
• Advancements in smartphone camera technology have decreased demand for dedicated replacement cameras.
• Consumers increasingly rely on online word-of-mouth for purchasing decisions.

Purpose of the Study:

• To analyze online word-of-mouth data to understand camera replacement decision-making.
• To leverage text mining techniques for deeper insights beyond numerical data.
• To provide actionable knowledge for camera companies and consumers.

Main Methods:

• Collection and analysis of online word-of-mouth data related to camera purchases.
• Application of text mining techniques to extract qualitative insights.
• Integration of both textual and numerical data for comprehensive analysis.

Main Results:

• Identified key themes and sentiments in online discussions about camera replacement.
• Quantified the impact of smartphone camera quality on consumer choices.
• Highlighted the critical role of online reviews in the customer journey.

Conclusions:

• Online word-of-mouth is a crucial factor in the camera replacement market.
• Text mining provides a powerful tool for understanding nuanced consumer preferences.
• Companies can utilize these insights to adapt strategies in a changing market.