A Study on the Application of Text Mining Techniques in the Marketing Strategy of DJI Action Cameras

DOI: https://doi.org/10.62517/jbdc.202501220

Author(s)

Pufeng Wu*, Jie Liu, Tan Yang

Affiliation(s)

School of Economics and Management, Xi’an University of Technology, Xi’an, Shaanxi, China *Corresponding Author

Abstract

This paper aims to conduct an in-depth study on the marketing strategies of the action camera industry, employing text mining techniques with DJI action cameras as a case study. The study collects customer reviews of the product from the JD platform using the Octoparse web scraper. It then performs word frequency list analysis, co-occurrence network analysis, and cluster analysis using KH Coder, along with sentiment analysis through ROST CM6. The results reveal that consumers’ focus on DJI action cameras primarily revolves around three key aspects: product performance (such as image quality, anti-shake functionality, portability), cost-effectiveness, and vendor services (such as logistics speed and customer service attitude). Sentiment analysis indicates that 95.66% of the reviews are positive, with consumers highly appreciating the product’s performance and user experience. Based on these findings, the paper suggests three marketing strategies: optimizing product performance, enhancing cost-effectiveness, and improving service systems. These strategies offer theoretical support and data references for the market practices within the action camera industry.

Keywords

Text Mining; Sentiment Analysis; Action Cameras; Marketing Strategies

References

[1]Luo Xiangdong, Qiang Wei, Zhang Xiying, et al. User evaluation and sentiment analysis of running shoes based on text mining. Journal of Silk, 2024, 61(06): 108-119. [2]Liu Jiakai, Li Min. Analysis of online reviews about silk quilts based on text mining: A case of Jingdong Mall. Journal of Silk, 2023, 60(08): 11-20. [3]Sun Tianxiao, Tian Yong, Chen Jinhui, et al. Analysis of airline passenger behavior based on text mining. Journal of Computer Applications, 2024, 44(S1): 49-53. [4]Wang Lei, An Jianing, Shi Shaoming. Study of flight risk assessment indicator based on text mining. Journal of Safety and Environment, 2025, 25(03): 825-834. [5]Ye Gui, Li Changfan, Li Jinpeng, et al. Analysis of Residents’ Low-Carbon Travel Intentions Based on Social Media Text Mining. Modern Urban Research, 2024, (10): 1-7+14. [6]Zheng Fu, He Lutong, Hong Linghui, et al. Study on Consumer Satisfaction of Tea Products on E-commerce Platforms Based on Text Mining—A Case Study of Online Reviews of Kuding Tea on Jingdong Mall. Trade Fair Economy, 2024, (04): 121-125. [7]Ding Yucheng, Li Hao. Text mining-based analysis of consumer reviews regarding medical protective clothing. Journal of Silk, 2023, 60(06): 47-57. [8]Li Yong, Tan Xiaoling, Chen Xiaoting, et al. Analysis of User Profiles of Agricultural Products Based on E-commerce Evaluation Data—A Case Study of Anhua Dark Tea. Rural Economy and Science-Technology, 2019, 30(19): 101-103+206. [9]Song Hongjuan, Zhao Lijuan, Jiang Yushi. Research on Tourism Marketing Value of Online Silk Road Video. Commercial Research, 2016, (10): 138-144. [10]Zhan Chuan. Analysis of Professionals’ Skill Demands Based on Text Mining ——Illustrated by the Case of E-commerce. Library Tribune, 2017, 37(05): 116-123. [11]Wang Chuhan, Zhang Chaozhi, Yang Yufei, et al. Travel Fast or Slow? --A Comparative Study on Emotional Differences of Tourists with Different Travel Paces Based on AI Itinerary Analysis. Journal of Central China Normal University (Natural Sciences), 2025, 59(03): 437-446.