{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:22:03Z","timestamp":1773804123797,"version":"3.50.1"},"reference-count":46,"publisher":"European Alliance for Innovation n.o.","issue":"4","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICST Transactions on Scalable Information Systems"],"abstract":"<jats:p>Today, in the e-commerce world, product reviews are a critical part of influencing consumer buying decisions and offer valuable insight to determine sales quality. But many current methods do not make efficient use of heterogeneous user-generated content (UGC) and those they predict with a unified model may ignore the different nature between various review types. In light of these limitations, this study introduces an integrated algorithmic framework that combines cutting-edge sentiment analyses and machine learning (ML) algorithms for sales quality prediction through automatic analysis of product reviews over the internet. The approach proposed will collect structured data from different sources during a systematic process and then consider the path of normalization, and sentiment analysis followed by feature selection to construct advanced prognosis models. The model proved highly effective, achieving an 88% accuracy rate in predicting sales quality. This strong performance indicates a significant correlation between sales performance and sentiment reviews. This new framework shows good promise that sentiment analysis in UGC can be used and deployed in e-commerce product evaluation and recommendation systems. Further research should investigate the integration of regional and temporal dynamics to improve model accuracy.<\/jats:p>","DOI":"10.4108\/eetsis.7216","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T13:59:04Z","timestamp":1752760744000},"source":"Crossref","is-referenced-by-count":1,"title":["Predicting product sales performance using various types of customer review data"],"prefix":"10.4108","volume":"12","author":[{"given":"Jinthusan","family":"Baskaran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mian Usman","family":"Sattar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamza","family":"Wazir Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2587","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"167253","doi-asserted-by":"crossref","unstructured":"[1] \u201cPredicting online product sales via online reviews, sentiments, and promotion strategies: A big data architecture and neural network approach\u201d, doi: 10.1108\/IJOPM-03-2015-0151.","DOI":"10.1108\/IJOPM-03-2015-0151"},{"key":"167254","doi-asserted-by":"crossref","unstructured":"[2] M. J. Schneider and S. Gupta, \u201cForecasting sales of new and existing products using consumer reviews: A random projections approach,\u201d Int J Forecast, vol. 32, no. 2, pp. 243\u2013256, Apr. 2016, doi: 10.1016\/J.IJFORECAST.2015.08.005.","DOI":"10.1016\/j.ijforecast.2015.08.005"},{"key":"167255","doi-asserted-by":"crossref","unstructured":"[3] A. Y. K. Chua and S. Banerjee, \u201cHelpfulness of user-generated reviews as a function of review sentiment, product type and information quality,\u201d Comput Human Behav, vol. 54, pp. 547\u2013554, Jan. 2016, doi: 10.1016\/J.CHB.2015.08.057.","DOI":"10.1016\/j.chb.2015.08.057"},{"key":"167256","doi-asserted-by":"crossref","unstructured":"[4] S. Krishnamoorthy, \u201cLinguistic features for review helpfulness prediction,\u201d Expert Syst Appl, vol. 42, no. 7, pp. 3751\u20133759, May 2015, doi: 10.1016\/J.ESWA.2014.12.044.","DOI":"10.1016\/j.eswa.2014.12.044"},{"key":"167257","doi-asserted-by":"crossref","unstructured":"[5] M. J. Schneider and S. Gupta, \u201cForecasting sales of new and existing products using consumer reviews: A random projections approach,\u201d Int J Forecast, vol. 32, no. 2, pp. 243\u2013256, Apr. 2016, doi: 10.1016\/J.IJFORECAST.2015.08.005.","DOI":"10.1016\/j.ijforecast.2015.08.005"},{"key":"167258","doi-asserted-by":"crossref","unstructured":"[6] N. Hu, I. Bose, N. S. Koh, and L. Liu, \u201cManipulation of online reviews: An analysis of ratings, readability, and sentiments,\u201d Decis Support Syst, vol. 52, no. 3, pp. 674\u2013684, Feb. 2012, doi: 10.1016\/J.DSS.2011.11.002.","DOI":"10.1016\/j.dss.2011.11.002"},{"key":"167259","doi-asserted-by":"crossref","unstructured":"[7] W. Duan, B. Gu, and A. B. Whinston, \u201cDo online reviews matter? \u2014 An empirical investigation of panel data,\u201d Decis Support Syst, vol. 45, no. 4, pp. 1007\u20131016, Nov. 2008, doi: 10.1016\/J.DSS.2008.04.001.","DOI":"10.1016\/j.dss.2008.04.001"},{"key":"167260","doi-asserted-by":"crossref","unstructured":"[8] J. Ghattas, P. Soffer, and M. Peleg, \u201cImproving business process decision making based on past experience,\u201d Decis Support Syst, vol. 59, no. 1, pp. 93\u2013107, Mar. 2014, doi: 10.1016\/J.DSS.2013.10.009.","DOI":"10.1016\/j.dss.2013.10.009"},{"key":"167261","doi-asserted-by":"crossref","unstructured":"[9] E. J. Lee and S. Y. Shin, \u201cWhen do consumers buy online product reviews? Effects of review quality, product type, and reviewer\u2019s photo,\u201d Comput Human Behav, vol. 31, no. 1, pp. 356\u2013366, Feb. 2014, doi: 10.1016\/J.CHB.2013.10.050.","DOI":"10.1016\/j.chb.2013.10.050"},{"key":"167262","doi-asserted-by":"crossref","unstructured":"[10] W. Duan, B. Gu, and A. B. Whinston, \u201cDo online reviews matter? \u2014 An empirical investigation of panel data,\u201d Decis Support Syst, vol. 45, no. 4, pp. 1007\u20131016, Nov. 2008, doi: 10.1016\/J.DSS.2008.04.001.","DOI":"10.1016\/j.dss.2008.04.001"},{"key":"167263","unstructured":"[11] \u201cBikram Prasain DEMAND, DEMAND FORECASTING, AND SUPPLY Case Company X,\u201d 2024."},{"key":"167264","unstructured":"[12] S. Venkataramanan, A. Kumar Reddy Sadhu, A. Kumar Reddy, and S. Engineering Manager, \u201cLeveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems,\u201d Australian Journal of Machine Learning Research & Applications, vol. 4, no. 1, pp. 267\u2013287, Jun. 2024, Accessed: Sep. 07, 2024. [Online]. Available: https:\/\/sydneyacademics.com\/index.php\/ajmlra\/article\/view\/77"},{"key":"167265","doi-asserted-by":"crossref","unstructured":"[13] W. Duan, B. Gu, and A. B. Whinston, \u201cDo online reviews matter? \u2014 An empirical investigation of panel data,\u201d Decis Support Syst, vol. 45, no. 4, pp. 1007\u20131016, Nov. 2008, doi: 10.1016\/J.DSS.2008.04.001.","DOI":"10.1016\/j.dss.2008.04.001"},{"key":"167266","doi-asserted-by":"crossref","unstructured":"[14] T. Macheka, E. S. Quaye, and N. Ligaraba, \u201cThe effect of online customer reviews and celebrity endorsement on young female consumers\u2019 purchase intentions\u201d, doi: 10.1108\/YC-05-2023-1749.","DOI":"10.1108\/YC-05-2023-1749"},{"key":"167267","doi-asserted-by":"crossref","unstructured":"[15] D. C. Wu\ue840, S. Zhong\ue840, R. T. R. Qiu\ue840, and J. Wu, \u201cAre customer reviews just reviews? Hotel forecasting using sentiment analysis,\u201d Special Issue Article Tourism Economics, vol. 2022, no. 3, pp. 795\u2013816, 2019, doi: 10.1177\/13548166211049865.","DOI":"10.1177\/13548166211049865"},{"key":"167268","doi-asserted-by":"crossref","unstructured":"[16] A. Y. L. Chong, B. Li, E. W. T. Ngai, E. Ch\u2019ng, and F. Lee, \u201cPredicting online product sales via online reviews, sentiments, and promotion strategies: A big data architecture and neural network approach,\u201d International Journal of Operations and Production Management, vol. 36, no. 4, pp. 358\u2013383, Apr. 2016, doi: 10.1108\/IJOPM-03-2015-0151\/FULL\/XML.","DOI":"10.1108\/IJOPM-03-2015-0151"},{"key":"167269","doi-asserted-by":"crossref","unstructured":"[17] M. J. Schneider and S. Gupta, \u201cForecasting sales of new and existing products using consumer reviews: A random projections approach,\u201d Int J Forecast, vol. 32, no. 2, pp. 243\u2013256, Apr. 2016, doi: 10.1016\/J.IJFORECAST.2015.08.005.","DOI":"10.1016\/j.ijforecast.2015.08.005"},{"key":"167270","doi-asserted-by":"crossref","unstructured":"[18] A. Y. K. Chua and S. Banerjee, \u201cHelpfulness of user-generated reviews as a function of review sentiment, product type and information quality,\u201d Comput Human Behav, vol. 54, pp. 547\u2013554, Jan. 2016, doi: 10.1016\/J.CHB.2015.08.057.","DOI":"10.1016\/j.chb.2015.08.057"},{"key":"167271","doi-asserted-by":"crossref","unstructured":"[19] S. Krishnamoorthy, \u201cLinguistic features for review helpfulness prediction,\u201d Expert Syst Appl, vol. 42, no. 7, pp. 3751\u20133759, May 2015, doi: 10.1016\/J.ESWA.2014.12.044.","DOI":"10.1016\/j.eswa.2014.12.044"},{"key":"167272","doi-asserted-by":"crossref","unstructured":"[20] M. J. Schneider and S. Gupta, \u201cForecasting sales of new and existing products using consumer reviews: A random projections approach,\u201d Int J Forecast, vol. 32, no. 2, pp. 243\u2013256, Apr. 2016, doi: 10.1016\/J.IJFORECAST.2015.08.005.","DOI":"10.1016\/j.ijforecast.2015.08.005"},{"key":"167273","doi-asserted-by":"crossref","unstructured":"[21] B. Alafwan, M. Siallagan, and U. S. Putro, \u201cComments Analysis on Social Media: A Review,\u201d EAI Endorsed Transactions on Scalable Information Systems, vol. 10, no. 6, Sep. 2023, doi: 10.4108\/EETSIS.3843.","DOI":"10.4108\/eetsis.3843"},{"key":"167274","doi-asserted-by":"crossref","unstructured":"[22] J. Ghattas, P. Soffer, and M. Peleg, \u201cImproving business process decision making based on past experience,\u201d Decis Support Syst, vol. 59, no. 1, pp. 93\u2013107, Mar. 2014, doi: 10.1016\/J.DSS.2013.10.009.","DOI":"10.1016\/j.dss.2013.10.009"},{"key":"167275","doi-asserted-by":"crossref","unstructured":"[23] J. Hartmann, M. Heitmann, C. Siebert, and C. Schamp, \u201cMore than a Feeling: Accuracy and Application of Sentiment Analysis,\u201d International Journal of Research in Marketing, vol. 40, no. 1, pp. 75\u201387, Mar. 2023, doi: 10.1016\/J.IJRESMAR.2022.05.005.","DOI":"10.1016\/j.ijresmar.2022.05.005"},{"key":"167276","doi-asserted-by":"crossref","unstructured":"[24] B. AlBadani, R. Shi, and J. Dong, \u201cA Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM,\u201d Applied System Innovation 2022, Vol. 5, Page 13, vol. 5, no. 1, p. 13, Jan. 2022, doi: 10.3390\/ASI5010013.","DOI":"10.3390\/asi5010013"},{"key":"167277","unstructured":"[25] B. Prasain, \u201cDemand, demand forecasting, and supply : case company X,\u201d 2024, Accessed: Sep. 07, 2024. [Online]. Available: http:\/\/www.theseus.fi\/handle\/10024\/855148"},{"key":"167278","unstructured":"[26] S. Paget, \u201cLocal Consumer Review Survey 2023\u201d."},{"key":"167279","doi-asserted-by":"crossref","unstructured":"[27] N. Groene and S. Zakharov, \u201cIntroduction of AI-based sales forecasting: how to drive digital transformation in food and beverage outlets,\u201d Discover Artificial Intelligence, vol. 4, no. 1, pp. 1\u201317, Dec. 2024, doi: 10.1007\/S44163-023-00097-X\/FIGURES\/6.","DOI":"10.1007\/s44163-023-00097-x"},{"key":"167280","unstructured":"[28] K. Kolehmainen, \u201cSales strategy development in growth companies,\u201d 2024, Accessed: Sep. 07, 2024. [Online]. Available: http:\/\/www.theseus.fi\/handle\/10024\/851306"},{"key":"167281","doi-asserted-by":"crossref","unstructured":"[29] M. S. Kasem, M. Hamada, and \u2022 Islam Taj-Eddin, \u201cCustomer profiling, segmentation, and sales prediction using AI in direct marketing,\u201d Neural Comput Appl, vol. 36, doi: 10.1007\/s00521-023-09339-6.","DOI":"10.1007\/s00521-023-09339-6"},{"key":"167282","doi-asserted-by":"crossref","unstructured":"[30] B. Biswas, M. K. Sanyal, and T. Mukherjee, \u201cAI-Based Sales Forecasting Model for Digital Marketing,\u201d https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJEBR.317888, vol. 19, no. 1, pp. 1\u201314, Jan. 1AD, doi: 10.4018\/IJEBR.317888.","DOI":"10.4018\/IJEBR.317888"},{"key":"167283","doi-asserted-by":"crossref","unstructured":"[31] S. Wang, Y. Lin, and G. Zhu, \u201cOnline reviews and high-involvement product sales: Evidence from offline sales in the Chinese automobile industry,\u201d Electron Commer Res Appl, vol. 57, p. 101231, Jan. 2023, doi: 10.1016\/J.ELERAP.2022.101231.","DOI":"10.1016\/j.elerap.2022.101231"},{"key":"167284","doi-asserted-by":"crossref","unstructured":"[32] H. Qahri-Saremi and A. R. Montazemi, \u201cEuropean Journal of Information Systems Negativity bias in the diagnosticity of online review content: the effects of consumers\u2019 prior experience and need for cognition Negativity bias in the diagnosticity of online review content: the effects of consumers\u2019 prior experience and need for cognition,\u201d 2022, doi: 10.1080\/0960085X.2022.2041372.","DOI":"10.1080\/0960085X.2022.2041372"},{"key":"167285","doi-asserted-by":"crossref","unstructured":"[33] M. Farshid, J. Paschen, T. Eriksson, and J. Kietzmann, \u201cGo boldly!: Explore augmented reality (AR), virtual reality (VR), and mixed reality (MR) for business,\u201d Bus Horiz, vol. 61, no. 5, pp. 657\u2013663, Sep. 2018, doi: 10.1016\/J.BUSHOR.2018.05.009.","DOI":"10.1016\/j.bushor.2018.05.009"},{"key":"167286","doi-asserted-by":"crossref","unstructured":"[34] A. D. Samala et al., \u201cGlobal Publication Trends in Augmented Reality and Virtual Reality for Learning: The Last Twenty-One Years,\u201d International Journal of Engineering Pedagogy, vol. 13, no. 2, p. 109, Mar. 2023, doi: 10.3991\/IJEP.V13I2.35965.","DOI":"10.3991\/ijep.v13i2.35965"},{"key":"167287","doi-asserted-by":"crossref","unstructured":"[35] T. Amirifar, S. Lahmiri, and M. K. Zanjani, \u201cAn NLP-Deep Learning Approach for Product Rating Prediction Based on Online Reviews and Product Features,\u201d IEEE Trans Comput Soc Syst, 2023, doi: 10.1109\/TCSS.2023.3290558.","DOI":"10.1109\/TCSS.2023.3290558"},{"key":"167288","doi-asserted-by":"crossref","unstructured":"[36] A. Y. K. Chua and S. Banerjee, \u201cHelpfulness of user-generated reviews as a function of review sentiment, product type and information quality,\u201d Comput Human Behav, vol. 54, pp. 547\u2013554, Jan. 2016, doi: 10.1016\/J.CHB.2015.08.057.","DOI":"10.1016\/j.chb.2015.08.057"},{"key":"167289","doi-asserted-by":"crossref","unstructured":"[37] C. Zhang, Y. X. Tian, and Z. P. Fan, \u201cForecasting sales using online review and search engine data: A method based on PCA\u2013DSFOA\u2013BPNN,\u201d Int J Forecast, vol. 38, no. 3, pp. 1005\u20131024, Jul. 2022, doi: 10.1016\/J.IJFORECAST.2021.07.010.","DOI":"10.1016\/j.ijforecast.2021.07.010"},{"key":"167290","doi-asserted-by":"crossref","unstructured":"[38] M. Akbarabadi and M. Hosseini, \u201cPredicting the helpfulness of online customer reviews: The role of title features,\u201d https:\/\/doi.org\/10.1177\/1470785318819979, vol. 62, no. 3, pp. 272\u2013287, Dec. 2018, doi: 10.1177\/1470785318819979.","DOI":"10.1177\/1470785318819979"},{"key":"167291","doi-asserted-by":"crossref","unstructured":"[39] M. Sattar, S. Palaniappan, A. Lokman, N. Shah, Z. Riaz, and U. Khalid, \u201cUser experience design in virtual reality medical training application,\u201d J Pak Med Assoc, vol. 71, no. 7, p. 1, 2019, doi: 10.5455\/JPMA.22992.","DOI":"10.5455\/JPMA.22992"},{"key":"167292","doi-asserted-by":"crossref","unstructured":"[40] F. R. Jim\u00e9nez and N. A. Mendoza, \u201cToo Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products,\u201d https:\/\/doi.org\/10.1016\/j.intmar.2013.04.004, vol. 27, no. 3, pp. 226\u2013235, Aug. 2013, doi: 10.1016\/J.INTMAR.2013.04.004.","DOI":"10.1016\/j.intmar.2013.04.004"},{"key":"167293","doi-asserted-by":"crossref","unstructured":"[41] M. Guha Majumder, S. Dutta Gupta, and J. Paul, \u201cPerceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis,\u201d J Bus Res, vol. 150, pp. 147\u2013164, Nov. 2022, doi: 10.1016\/J.JBUSRES.2022.06.012.","DOI":"10.1016\/j.jbusres.2022.06.012"},{"key":"167294","doi-asserted-by":"crossref","unstructured":"[42] A. Ghaffar et al., \u201cMulti-stage intelligent smart lockdown using sir model to control covid 19,\u201d Intelligent Automation and Soft Computing, vol. 28, no. 2, pp. 429\u2013445, 2021, doi: 10.32604\/iasc.2021.014685.","DOI":"10.32604\/iasc.2021.014685"},{"key":"167295","doi-asserted-by":"crossref","unstructured":"[43] N. Kushwaha, B. Singh, and S. Agrawal, \u201cManifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism,\u201d EAI Endorsed Transactions on Scalable Information Systems, vol. 11, no. 6, Apr. 2024, doi: 10.4108\/EETSIS.5698.","DOI":"10.4108\/eetsis.5698"},{"key":"167296","doi-asserted-by":"crossref","unstructured":"[44] S. Ansari and S. Gupta, \u201cCustomer perception of the deceptiveness of online product reviews: A speech act theory perspective,\u201d Int J Inf Manage, vol. 57, p. 102286, Apr. 2021, doi: 10.1016\/J.IJINFOMGT.2020.102286.","DOI":"10.1016\/j.ijinfomgt.2020.102286"},{"key":"167297","doi-asserted-by":"crossref","unstructured":"[45] W. Xu, Y. Cao, and R. Chen, \u201cA multimodal analytics framework for product sales prediction with the reputation of anchors in live streaming e-commerce,\u201d Decis Support Syst, vol. 177, p. 114104, Feb. 2024, doi: 10.1016\/J.DSS.2023.114104.","DOI":"10.1016\/j.dss.2023.114104"},{"key":"167298","doi-asserted-by":"crossref","unstructured":"[46] M. M. Mariani, M. Borghi, and B. Laker, \u201cDo submission devices influence online review ratings differently across different types of platforms? A big data analysis,\u201d Technol Forecast Soc Change, vol. 189, p. 122296, Apr. 2023, doi: 10.1016\/J.TECHFORE.2022.122296.","DOI":"10.1016\/j.techfore.2022.122296"}],"container-title":["ICST Transactions on Scalable Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/download\/7216\/3642","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/download\/7216\/3642","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T13:59:14Z","timestamp":1752760754000},"score":1,"resource":{"primary":{"URL":"https:\/\/publications.eai.eu\/index.php\/sis\/article\/view\/7216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,7,15]]}},"URL":"https:\/\/doi.org\/10.4108\/eetsis.7216","relation":{},"ISSN":["2032-9407"],"issn-type":[{"value":"2032-9407","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}