Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era

Main Article Content

Khin Sandar Kyaw
Praman Tepsongkroh
Chanwut Thongkamkaew
Farida Sasha


Since trading has been transformed into online platforms, marketing strategies have adapted to digital systems in order to enhance the Customer Relationship Management (CRM) in the E-commerce era. E-commerce systems are the most widely used digital platforms where customer information including personal, and behavioral information, flows as a big data stream. Conducting business intelligent observation on digital big data assists to improve digital marketing policy through the customer intention prediction, decision-making to advertise based on the target group clustering, and customer assist recommendation. To discover the business intelligent, sentiment analysis technology can assist as a solution to understand the customer behavior through the opinion mining where the natural language processing, text analysis, computational linguistics, and biometrics are conducted to analysis the customer information and feedbacks, for smart digital marketing applications. This research observes the applications of sentiment analysis in E-commerce systems as a comprehensive study, and the critical role of discovering business intelligent for smart digital marketing in E-commerce platforms is pointed out according to the technical perspective. Furthermore, the concept of a business intelligent framework integrated with the modelling of decision-making, prediction, and recommendation systems using the contribution of hybrid feature selection which is based on rule-based and machine learning-based sentiment analysis, is proposed for the future innovative smart digital marketing trend.


Download data is not yet available.

Article Details

How to Cite
Khin Sandar Kyaw, Tepsongkroh, P. ., Thongkamkaew, C. ., & Sasha, F. . (2023). Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era. Asia Social Issues, 16(3), e252965.
Research Article


Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324.

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.

Ahuja, R., Chug, A., Kohli, S., Gupta, S., & Ahuja, P. (2019). The impact of features extraction on the sentiment analysis. Procedia Computer Science, 152, 341-348.

AL-Sharuee, M. T., Liu, F., & Pratama, M. (2021). Sentiment analysis: dynamic and temporal clustering of product reviews. Applied Intelligence, 51(1), 51-70.

Alshaer, H. N., Otair, M. A., Abualigah, L., Alshinwan, M., & Khasawneh, A. M. (2021). Feature selection method using improved CHI square on Arabic text classifiers: Analysis and application. Multimedia Tools and Applications, 80(7), 10373-10390.

Aulawi, H., Karundeng, E., Kurniawan, W. A., Septiana, Y., & Latifah, A. (2021). Consumer sentiment analysis to E-commerce in the Covid-19 pandemic era (pp. 1-5). In Proceedings of the 2021 International Conference on ICT for Smart Society (ICISS). Bandung, Indonesia: IEEE.

Badugu, S. (2022). A comparative study on classification algorithms using different feature extraction and vectorization techniques for text. Turkish Online Journal of Qualitative Inquiry, 12(7), 8216-8225.

Bandyopadhyay, S., Thakur, S. S., & Mandal, J. K. (2021). Product recommendation for E-commerce business by applying principal component analysis (PCA) and K-means clustering: Benefit for the society. Innovations in Systems and Software Engineering, 17(1), 45-52.

Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018, October). Sentiment analysis about E-Commerce from Tweets using decision tree, K-Nearest Neighbor, and Naïve Bayes (pp. 1-6). In Proceedings of the 2018 International Conference on Orange Technologies (ICOT). Bandung, Indonesia: IEEE.

Behera, R. K., Gunasekaran, A., Gupta, S., Kamboj, S., & Bala, P. K. (2020). Personalized digital marketing recommender engine. Journal of Retailing and Consumer Services, 53, 101799.

Berka, P. (2020). Sentiment analysis using rule-based and case-based reasoning. Journal of Intelligent Information Systems, 55(1), 51-66.

Bertea, A. F. (2019). Data web mining in E-commerce: Progress and perspectives (pp. 85-90). In Proceedings of the 18th International Conference on Informatic in Economy Education. Bucharest, Romania: Research and Business Technologies.

Bineet Kumar Jha, S. G. G. V. K. R. (2021). Sentiment analysis for E-commerce products using natural language processing. Annals of the Romanian Society for Cell Biology, 25(5), 166-175.

Bird, S., & Loper, E. (2004). NLTK: The Natural Language Toolkit (pp. 69-72). In Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions. Sydney: Association for Computational Linguistics

Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.

Bommert, A., Sun, X., Bischl, B., Rahnenführer, J., & Lang, M. (2020). Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics and Data Analysis, 143, 106839.

Bueno, I., Carrasco, R. A., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320.

Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms (pp.161-168). In Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, PA: ACM.

Choi, G., Oh, S., & Kim, H. (2020). Improving document-level sentiment classification using importance of sentences. Entropy, 22(12), 1336.

Dey, A., Jenamani, M., & Thakkar, J. J. (2018). Senti-N-Gram: An n-gram lexicon for sentiment analysis. Expert Systems with Applications, 103, 92-105.

Dey, S., Wasif, S., Tonmoy, D. S., Sultana, S., Sarkar, J., & Dey, M. (2020). A comparative study of support vector machine and Naive Bayes classifier for sentiment analysis on Amazon product reviews (pp. 217-220). In Proceedings of the 2020 International Conference on Contemporary Computing and Applications (IC3A). Lucknow, India: IEEE.

Gao, J., Wu, A., Li, M., Huang, C.-N., Li, H., Xia, X., & Qin, H. (2004). Adaptive Chinese word segmentation. Retrieved from

Gao, W., Hu, L., & Zhang, P. (2020). Feature redundancy term variation for mutual information-based feature selection. Applied Intelligence, 50(4), 1272-1288.

Gharzouli, M., Hamama, A. K., & Khattabi, Z. (2021). Topic-based sentiment analysis of hotel reviews. Current Issues in Tourism, 1-8.

Gil-Gomez, H., Guerola-Navarro, V., Oltra-Badenes, R., & Lozano-Quilis, J. A. (2020). Customer relationship management: digital transformation and sustainable business model innovation. Economic Research-Ekonomska Istrazivanja , 33(1), 2733-2750.

Gokalp, O., Tasci, E., & Ugur, A. (2020). A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification. Expert Systems with Applications, 146, 113176.

Han, H., Zhang, J., Yang, J., Shen, Y., & Zhang, Y. (2018). Generate domain-specific sentiment lexicon for review sentiment analysis. Multimedia Tools and Applications, 77(16), 21265-21280.

Hawlader, M., Ghosh, A., Raad, Z. K., Chowdhury, W. A., Shehan, M. S. H., & Ashraf, F. Bin. (2021). Amazon product reviews: Sentiment analysis using supervised learning algorithms (pp. 1-6). In Proceeding of the 2021 International Conference on Electronics, Communications, and Information Technology (ICECIT). Khulna, Bangladesh: IEEE.

Huang, M., Xie, H., Rao, Y., Liu, Y., Poon, L. K. M., & Wang, F. L. (2020). Lexicon-based sentiment convolutional neural networks for online review analysis. IEEE Transactions on Affective Computing.14(8), pp 1-12.

Jabbar, J. (2019). Real-time sentiment analysis on E-commerce application (pp. 391-396). In Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC). Banff, AB, Canada: IEEE.

Jiang, Y., Wang, H., & Yi, T. (2021). Evaluation of product reviews based on text sentiment analysis (pp. 1-8). In Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS'21). Chongqing, China: ACM.

Kadhim, A. I. (2019). Term weighting for feature extraction on Twitter: A comparison between BM25 and TF-IDF (pp. 124-128). In Proceedings of the 2019 International Conference on Advanced Science and Engineering (ICOASE 2019). Zakho - Duhok, Iraq: IEEE.

Kang, Y., & Zhou, L. (2017). RubE: Rule-based methods for extracting product features from online consumer reviews. Information and Management, 54(2), 166-176.

Karegowda, A. G., Manjunath, A. S., Ratio, G., & Evaluation, C. F. (2010). Comparative study of attribute selection using Gain Ratio. International Journal of Information Technology and Knowledge Management, 2(2), 271-277.

Karthik, R. V., & Ganapathy, S. (2021). A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce. Applied Soft Computing, 108, 107396.

Khoa Dam, N. A. (2019). Marketing intelligence from data mining perspective - A literature review. International Journal of Innovation, Management and Technology, 10(5), 184-190.

Kumar, C. S. P., & Babu, L. D. D. (2020). Evolving dictionary based sentiment scoring framework for patient authored text. Evolutionary Intelligence, 14(2), 657-667.

Lee, S. Y., Qiu, L., & Whinston, A. (2018). Sentiment manipulation in online platforms: An analysis of movie tweets. Production and Operations Management, 27(3), 393-416.

Li, X., Sun, X., Xu, Z., & Zhou, Y. (2021). Explainable sentence-level sentiment analysis for amazon product reviews (pp. 88-94). In Proceedings of the 2021 5th International Conference on Imaging, Signal Processing and Communications (ICISPC). Kumamoto, Japan: IEEE.

Lian, Q. (2021). Personalized recommendation algorithm based on online comment sentiment analysis. Journal of Physics: Conference Series, 1873(1), 012086.

liu, jingfang, zhou, yingyi, jiang, xiaoyan, & zhang, wei. (2020). Consumers’ satisfaction factors mining and sentiment analysis of B2C online pharmacy reviews. BMC Medical Informatics and Decision Making, 20(1), 1-13.

Liu, P., Zhang, L., & Gulla, J. A. (2021). Multilingual review-aware deep recommender system via aspect-based sentiment analysis. ACM Transactions on Information Systems, 39(2), 1-33.

Lye, S. H., & Teh, P. L. (2022). Customer intent prediction using sentiment analysis techniques (pp. 185-190). In Proceedings of the 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Cracow, Poland: IEEE.

Mai, L., & Le, B. (2021). Joint sentence and aspect-level sentiment analysis of product comments. Annals of Operations Research, 300(2), 493-513.

Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135-154.

Manoharan, G., & Narayanan, S. (2021). A research study to investigate the feasibility of digital marketing strategies in advertising. PalArch’s Journal of Archaeology of Egyptology, 18(9), 450-456.

Michalak, K., & Kwasnicka, H. (2010). Correlation based feature selection method. International Journal of Bio-Inspired Computation, 2(5), 319-332.

Miklosik, A., Kuchta, M., Evans, N., & Zak, S. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. IEEE Access, 7, 85705-85718.

Naim, A. (2021). Applications of marketing framework in business practices. International Journal of Innovative Analyses and Emerging Technology, 1(6), 171-186.

Nawaz, Z., Zhao, C., Nawaz, F., Safeer, A. A., & Irshad, W. (2021). Role of artificial neural networks techniques in development of market intelligence: A study of sentiment analysis of eWOM of a women’s clothing company. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1862-1876.

Necula, S. C., Pavaloaia, V. D., Strîmbei, C., & Dospinescu, O. (2018). Enhancement of E-commerce websites with semantic web technologies. Sustainability, 10(6), 1-15.

Nursetyo, A., Setiadi, D. R. I. M., & Subhiyakto, E. R. (2018). Smart chatbot system for E-commerce assitance based on AIML (pp. 641-645). In Proceedings of the 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI 2018). Yogyakarta, Indonesia: IEEE.

Osman, N. A., Noah, S. A. M., & Darwich, M. (2019). Contextual sentiment based recommender system to provide recommendation in the electronic products domain. International Journal of Machine Learning and Computing, 9(4), 425-431.

Pugsee, P., & Niyomvanich, M. (1970). Sentiment analysis of food recipe comments. ECTI Transactions on Computer and Information Technology, 9(2), 182-193.

Qaiser, S., & Ali, R. (2018). Text mining: Use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29.

Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 106935.

Santos, F., & Martinho, R. (2021). Architectural challenges on the integration of E-commerce and ERP systems: A case study. 1, 313-319.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.

Singh, V. K., Piryani, R., Waila, P., & Devaraj, M. (1970). Computing sentiment polarity of texts at document and aspect evels. ECTI Transactions on Computer and Information Technology, 8(1), 67-79.

Sohail, O., Elahi, I., Ijaz, A., Karim, A., & Kamiran, F. (2018). Text classification in an under-resourced language via lexical normalization and feature pooling. In Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society (PACIS 2018). Japan: Association for Information Systems.

Solangi, Y. A., Solangi, Z. A., Aarain, S., Abro, A., Mallah, G. A., & Shah, A. (2019). Review on Natural Language Processing (NLP) and its toolkits for opinion mining and sentiment analysis (pp. 22-23). In Proceedings of the 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS 2018). Bangkok, Thailand: IEEE.

Subhashini, L. D. C. S., Li, Y., Zhang, J., Atukorale, A. S., & Wu, Y. (2021). Mining and classifying customer reviews: A survey. In Artificial Intelligence Review, 54, 6343-6389.

Taj, S., Shaikh, B. B., & Fatemah Meghji, A. (2019). Sentiment analysis of news articles: A lexicon based approach (pp. 1-5). In Proceedings of the 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (ICoMET 2019). Sukkur, Pakistan: IEEE.

Tanha, J., van Someren, M., & Afsarmanesh, H. (2017). Semi-supervised self-training for decision tree classifiers. International Journal of Machine Learning and Cybernetics, 8(1), 355-370.

Tudoran, A. A. (2022). A machine learning approach to identifying decision-making styles for managing customer relationships. Electronic Markets, 1-24.

Utiu, N., & Ionescu, V. S. (2018). Learning web content extraction with DOM features (pp. 5–11). In Proceedings of the 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP 2018). Cluj-Napoca, Romania: IEEE.

Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing and Management, 50(1), 104-112.

Wan, Q., Xu, X., Zhuang, J., & Pan, B. (2021). A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data. Expert Systems with Applications, 185(932), 115629.

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. In Artificial Intelligence Review, 1-50.

Xie, X., Ge, S., Hu, F., Xie, M., & Jiang, N. (2019). An improved algorithm for sentiment analysis based on maximum entropy. Soft Computing, 23(2), 599-611.

Xu, F., Pan, Z., & Xia, R. (2020). E-commerce product review sentiment classification based on a Naïve Bayes continuous learning framework. Information Processing and Management, 57(5), 102221.

Yang, D., & Thiengburanathum, P. (2020). A comparison of open source web crawlers for E-commerce websites (pp. 200-205). In Proceedings of the 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT and NCON 2020). Pattaya, Thailand: IEEE.

Zarisfi Kermani, F., Eslami, E., & Sadeghi, F. (2019). Global filter–wrapper method based on class-dependent correlation for text classification. Engineering Applications of Artificial Intelligence, 85, 619-633.

Zhai, Y., Wang, Z., Zeng, H., & Hu, Z. (2021). Social media opinion leader identification based on sentiment analysis (pp. 436-440). In Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing (BIC 2021). Harbin, China: Association for Computing Machinery.

Zhang, H., Zang, Z., Zhu, H., Uddin, M. I., & Amin, M. A. (2022). Big data-assisted social media analytics for business model for business decision making system competitive analysis. Information Processing and Management, 59(1), 102762.

Zhang, Yanrong, Sun, J., Meng, L., & Liu, Y. (2020). Sentiment analysis of E-commerce text reviews based on sentiment dictionary (pp. 1346-1350). In Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). Dalian, China: IEEE.

Zhang, Yin, Abbas, H., & Sun, Y. (2019). Smart E-commerce integration with recommender systems. Electronic Markets, 29(2), 219-220.

Zhang, Yin, Jin, R., & Zhou, Z. H. (2010). Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 1(1-4), 43-52.

Zhang, Y., Mukherjee, R., & Soetarman, B. (2013). Concept extraction and E-commerce applications. Electronic Commerce Research and Applications, 12(4), 289-296.

Zhou, Q., Xu, Z., & Yen, N. Y. (2019). User sentiment analysis based on social network information and its application in consumer reconstruction intention. Computers in Human Behavior, 100, 177-183.

Zikang, H., Yong, Y., Guofeng, Y., & Xinyu, Z. (2020). Sentiment analysis of agricultural product E-commerce review data based on deep learning (pp. 1-7). In Proceedings of the 2020 International Conference on Internet of Things and Intelligent Applications (ITIA 2020). Zhenjiang, China: IEEE.

Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H., & Cannataro, M. (2020). Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), 1-32.