Sensor Deployed Agricultural Land for E-Commerce Platform withClustering of Nodes in the Network
DOI:
https://doi.org/10.69996/jsihs.2024013Keywords:
Sensor Environment, Agriculture, E-commerce, Clustering, Network EnvironmentAbstract
A sensor-deployed agricultural land for an e-commerce platform integrates IoT sensors and
wireless networks to monitor crop health, soil conditions, and environmental factors in real-time. These sensors collect data on moisture levels, temperature, humidity, and nutrient content, which is transmitted to a centralized platform. Through clustering of sensor nodes within the network, efficient data aggregation and communication are achieved, reducing energy consumption and improving network performance. This paper presents a comprehensive analysis of agricultural product e-commerce leveraging advanced data analysis techniques. By employing methods such as word frequency analysis, sentiment analysis, and clustering algorithms, we explore the multifaceted landscape of online agricultural markets. This study investigates the prevalence of key themes and trends in agricultural e-commerce, ranging from product categories to digital marketing strategies. Through sentiment analysis, we uncover nuanced patterns in consumer sentiment, providing insights into effective market positioning strategies. Our findings reveal that "agricultural products" emerge as the most prevalent theme, accounting for 8.5% of the dataset. Additionally, sentiment analysis indicates a positive sentiment towards e-commerce platforms, with a precision of 0.85 and an F1-score of 0.83. Clustering analysis delineates five distinct clusters, each representing different facets of agricultural e-commerce, with Cluster 1 focusing on agricultural products and Cluster 2 on digital marketing strategies.
References
[1] Y. Huang, “Research Progress and Hot Topics of Agricultural Products E-commerce in China:--A Visual Analysis Based on CNKI,” Journal of Innovation and Development, vol.2, no.3, pp. 125-132, 2023.
[2] J. Ke, Y. Wang, M. Fan, X. Chen, W. Zhang et al., “Discovering e-commerce user groups from online comments: An emotional correlation analysis-based clustering method,” Computers and Electrical Engineering, vol.113, ppp.109035, 2024.
[3] H. Xia, J. Weng, J.Z. Zhang and Y. Gao, “Rural E-commerce model with attention mechanism: role of Li ziqi’s short videos from the perspective of heterogeneous knowledge management,” Journal of Global Information Technology Management, vol.25, no.2, pp.118-136, 2022.
[4] Z. Shen, “Mining sustainable fashion e-commerce: Social media texts and consumer behaviors,” Electronic Commerce Research, vol.23, no.2, pp.949-971, 2023.
[5] X. Liu, S. Cao and X. Wang, “Analysis And Research on User of Agricultural Products Live Streaming of E-Commerce Under the Background of China's Rural Revitalization,” In Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, Qingdao, China, pp. 17-19, 2022.
[6] X. Bai, E. C. X. Aw, G. W. H. Tan and K. B. Ooi, “Livestreaming as the next frontier of ecommerce: A bibliometric analysis and future research agenda,” Electronic Commerce Research and Applications, pp.101390, 2024.
[7] H. Tan, S. Peng, J. X.Liu, C. P. Zhu and F. Zhou, “Generating personas for products on social media: a mixed method to analyze online users,” International Journal of Human–Computer Interaction, vol.38, no.13, pp.1255-1266, 2022.
[8] E. Daskalakis, K. Remoundou, N. Peppes, T. Alexakis, K. Demestichas et al., “Applications of fusion techniques in e-commerce environments: A literature review,” Sensors, vol.22, no.11, pp.3998, 2022.
[9] W. Kim, K. Nam and Y. Son, “Categorizing affective response of customer with novel explainable clustering algorithm: The case study of Amazon reviews,” Electronic Commerce Research and Applications, vol.58, pp.101250, 2023.
[10] A. L. Karn, R. K. Karna, B. R. Kondamudi, G. Bagale, D.A. Pustokhin et al., “Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis,” Electronic Commerce Research, vol.23, no.1, pp.279-314, 2023.
[11] J. Wu, J. Zhang and N. Zhao, “How to boost e-commerce for poverty alleviation? A perspective on competitiveness analysis using online reviews,” Electronic Commerce Research, pp.1-32, 2023.
[12] D. K. Sharma, B. Singh, S. Agarwal, H. Kim and R. Sharma, “Sarcasm detection over social media platforms using hybrid auto-encoder-based model,” Electronics, vol. 11, no.18, pp.2844, 2022.
[13] A. A. Bhalerao, B. R. Naiknaware, R. R. Manza, V. Bagal and S. K. Bawiskar, “Social Media Mining Using Machine Learning Techniques as a Survey,” In International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022), Atlantis Press, pp. 874- 889, 2023.
[14] S. Kushwah, N. Sharma and S. Das, “Novel E-Focused Crawler and Enhanced k-mean (n-gram) clustering technique for Automatic classification of attribute level customer healthcare sentiments,” Journal of Algebraic Statistics, vol.13, no.2, 2022.
[15] Z. Miloradovic, J. Kovacevic, J. Miocionovic, I. Djekic, N. Kljajevic and N. Smigic, “E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps,” Heliyon, vol.10, no.6, 2024.
[16] L. Xinwei, Y.K. Tse and F. Fastoso, “Unleashing the power of social media data in business decision making: an exploratory study,” Enterprise Information Systems, vol.18, no.1, pp. 2243603, 2024.
[17] T. Chen, C. Tong, Y. Bai, J. Yang, G. Cong et al., “Analysis of the public opinion evolution on the normative policies for the live streaming e-commerce industry based on online comment mining under COVID-19 epidemic in China,” Mathematics, vol.10, no.18, pp.3387, 2022.
[18] M. R. R. Rana, S. U. Rehman, A. Nawaz, T. Ali, A. Imran et al., “Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework,” Comput. Syst. Sci. Eng., vol.46, no.2, pp.2415-2428, 2023.
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