Mô hình khám phá ý kiến khách hàng về ứng dụng IoT tại các cửa hàng bán lẻ dựa trên phương pháp phân tích quan điểm và máy học kết hợp

Al-Azani, S., & El-Alfy, E.-S. M. (2017). Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text. Procedia Computer Science, 109(2017), 359-366. doi:10.1016/j.procs.2017.05.365

Ali, Z. H., Ali, H. A., & Badawy, M. M. (2015). Internet of Things (IoT): Definitions, challenges, and recent research directions. International Journal of Computer Applications, 128(1), 37-47.

Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97-114.

Athar, A., Ali, S., Sheeraz, M. M., Bhattachariee, S., & Kim, H.-C. (2021). Sentimental analysis of movie reviews using soft voting ensemble-based machine learning. Paper presented at the 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS), Gandia, Spain.

Bach, S. T., & Dang, H. T. (2019). Internet kết nối vạn vật ở Việt Nam: Thực trạng và giải pháp phát triển [Internet of Things in Vietnam: Current status and development solutions]. Tạp chí Khoa học-Công nghệ, 22(1/2A), 22-24.

Balaji, M. S., & Roy, S. K. (2017). Value co-creation with Internet of things technology in the retail industry. Journal of Marketing Management, 33(1/2), 7-31. doi:10.1080/0267257X.2016.1217914

Bok, B. t. (2016). Innovating the retail industry: An IoT approach. Truy cập ngày 10/10/2022 tại http://essay.utwente.nl/69982/ 

BusinessWire. (2022). Global IoT in retail market report to 2031. Truy cập ngày 10/10/2022 tại https://www.businesswire.com/news/home/20221111005333/en/Global-IoT-in-Retail-Market-Report-to-2031---Featuring-Huawei-Technologies-SAP-Google-and-NEC-Among-Others---ResearchAndMarkets.com

Camacho-Collados, J., & Pilehvar, M. T. (2018). On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis. Paper presented at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium.

Carbone, L. P. (1998). Total customer experience drives value. Management Review, 87(7), 62-63.

Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. doi:10.1609/icwsm.v8i1.14550

Kumar, K. S., Desai, J., & Majumdar, J. (2016). Opinion mining and sentiment analysis on online customer review. Paper presented at the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), United States.

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-67. doi:10.2200/s00416ed1v01y201204hlt016

Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116-157.

Miazi, M. N. S., Erasmus, Z., Razzaque, M. A., Zennaro, M., & Bagula, A. (2016). Enabling the Internet of Things in developing countries: Opportunities and challenges. Paper presented at the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh.

Mitra, A. (2020). Sentiment analysis using machine learning approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies, 2(3), 145-152. doi:10.36548/jucct.2020.3.004

Nikolić, N., Grljević, O., & Kovačević, A. (2020). Aspect-based sentiment analysis of reviews in the domain of higher education. The Electronic Library, 38(1), 44-64. doi:10.1108/EL-06-2019-0140

Obaidat, I., Mohawesh, R., Al-Ayyoub, M., Smadi, M. A., & Jararweh, Y. (2015). Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches. Paper presented at the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), United States.

Özçift, A. (2020). Medical sentiment analysis based on soft voting ensemble algorithm. Yönetim Bilişim Sistemleri Dergisi, 6(1), 42-50.

Pantano, E. (2014). Innovation drivers in retail industry. International Journal of Information Management, 34(3), 344-350. doi:10.1016/j.ijinfomgt.2014.03.002

Pantano, E., & Timmermans, H. (2014). What is smart for retailing? Procedia Environmental Sciences, 22(2014), 101-107. doi:10.1016/j.proenv.2014.11.010

Patil, K. (2016). Retail adoption of Internet of Things: Applying TAM model. Paper presented at the 2016 International Conference on Computing, Analytics and Security Trends (CAST), Pune, India.

Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143-157. doi:10.1016/j.joi.2009.01.003

Reddy, G. T., Bhattacharya, S., Ramakrishnan, S. S., Chowdhary, C. L., Hakak, S., Kaluri, R., & Reddy, M. P. K. (2020). An ensemble based machine learning model for diabetic retinopathy classification. Paper presented at the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.

Reddy, T. (2015). 13 retail companies using data to revolutionize online & offline shopping experiences. Truy cập ngày 10/10/2022 tại https://www.smartdatacollective.com/retail-companies-using-data-revolutionize-shopping-experiences/

UKEssays. (November, 2018). The Internet of Things: Revolutionizing the retail industry. Truy cập ngày 10/10/2022 tại https://www.ukessays.com/essays/computer-science/the-internet-of-things-revolutionizing-the-retail-industry.php?vref=1

Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37. doi:10.1007/s10115-007-0114-2

Xia, R., Zong, C., & Li, S. (2011). Ensemble of feature sets and classification algorithms for sentiment classification. Information Sciences, 181(6), 1138-1152. doi:10.1016/j.ins.2010.11.023

Zhou, Z.-H. (2021). Ensemble learning. In Z.-H. Zhou (Ed.), Machine learning (pp. 181-210). Singapore: Springer.

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