{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:55:07Z","timestamp":1781106907631,"version":"3.54.1"},"reference-count":69,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<p>In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.<\/p>","DOI":"10.4018\/ijdwm.2019100102","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T10:16:59Z","timestamp":1568369819000},"page":"21-47","source":"Crossref","is-referenced-by-count":41,"title":["The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying"],"prefix":"10.4018","volume":"15","author":[{"given":"Conor","family":"Gallagher","sequence":"first","affiliation":[{"name":"Letterkenny Institute of Technology, Donegal, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eoghan","family":"Furey","sequence":"additional","affiliation":[{"name":"Letterkenny Institute of Technology, Donegal, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Curran","sequence":"additional","affiliation":[{"name":"Ulster University, Derry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJDWM.2019100102-0","unstructured":"ACSI. (2018). American Customer Satisfaction Index. Retrieved from http:\/\/www.theacsi.org\/about-acsi\/the-science-of-customer-satisfaction"},{"key":"IJDWM.2019100102-1","unstructured":"Ajenstat, F. (2017). Tableau five years a leader in Gartner\u2019s Magic Quadrant for Analytics. Tableau. Retrieved from https:\/\/www.tableau.com\/about\/blog\/2017\/2\/tableau-five-years-leader-gartners-magic-quadrant-analytics-66133"},{"key":"IJDWM.2019100102-2","unstructured":"Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. arXiv:1707.02919"},{"issue":"1","key":"IJDWM.2019100102-3","first-page":"14","article-title":"The impact of customer satisfaction on customer loyalty.","volume":"5","author":"A.Arokiasamy","year":"2013","journal-title":"Journal of Commerce"},{"key":"IJDWM.2019100102-4","unstructured":"Aswani, S. (2017). Analyzing Customer Feedback Data: Manual Analysis vs NLP. Clarabridge. Retrieved from https:\/\/www.clarabridge.com\/blog\/analyzing-customer-feedback-data-manual-analysis-vs-nlp\/"},{"issue":"2","key":"IJDWM.2019100102-5","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/10580530801941058","article-title":"Management support with structured and unstructured data\u2014an integrated business intelligence framework.","volume":"25","author":"H.Baars","year":"2008","journal-title":"Information Systems Management"},{"key":"IJDWM.2019100102-6","doi-asserted-by":"crossref","unstructured":"Balahur, A. & Montoyo, A. (2008). A feature dependent method for opinion mining and classification.","DOI":"10.1109\/NLPKE.2008.4906796"},{"key":"IJDWM.2019100102-7","unstructured":"LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value.MIT Sloan Management Review, 52(2), 21\u201332."},{"key":"IJDWM.2019100102-8","first-page":"1","article-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"S.Bird","year":"2009","journal-title":"Natural language processing with Python: analyzing text with the natural language toolkit"},{"key":"IJDWM.2019100102-9","unstructured":"Broan, S.E. & Steger, A.J. (2013). Key performance indicators are not just about profit. CFMA. Retrieved from http:\/\/www.cfma.org\/content.cfm?ItemNumber=1899"},{"key":"IJDWM.2019100102-10","unstructured":"Brownlee, J. (2016). Support Vector Machines for Machine Learning. Retrieved from https:\/\/machinelearningmastery.com\/support-vector-machines-for-machine-learning\/"},{"key":"IJDWM.2019100102-11","unstructured":"Cappelli, A. (2017). Natural Language Processing with Stanford CoreNLP. Retrieved from https:\/\/cloudacademy.com\/blog\/natural-language-processing-stanford-corenlp-2\/"},{"key":"IJDWM.2019100102-12","unstructured":"Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Sherer, C., & Wirth, R. (2000). Cross industry standard process for data mining (CRISP-DM) 1.0."},{"key":"IJDWM.2019100102-13","doi-asserted-by":"publisher","DOI":"10.2307\/41703503"},{"key":"IJDWM.2019100102-14","unstructured":"Chetviorkin, I. & Loukachevitch, N. (2013). Evaluating sentiment analysis systems in Russian."},{"key":"IJDWM.2019100102-15","doi-asserted-by":"publisher","DOI":"10.1002\/aris.1440370103"},{"key":"IJDWM.2019100102-16","unstructured":"Clarabridge. (2015). Retrieved from www.clarabridge.com"},{"key":"IJDWM.2019100102-17","unstructured":"Cran Project. (2018). CRAN Task View: Natural Language Processing. Retrieved from https:\/\/cran.r-project.org\/web\/views\/NaturalLanguageProcessing.html"},{"key":"IJDWM.2019100102-18","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1872"},{"key":"IJDWM.2019100102-19","unstructured":"Department of Industry, Australian Government. (2018). Understand your customers. [REMOVED HYPERLINK FIELD]Retrieved from https:\/\/www.business.gov.au\/info\/plan-and-start\/start-your-business\/what-is-customer-service\/understand-your-customers"},{"key":"IJDWM.2019100102-20","unstructured":"Devika, M. D. C, S. & Ganesha, A., 2016. Sentiment Analysis:A Comparative Study On Different Approaches. Chennai, Tamil Nadu, India, Department of CSE, Vidya Academy of Science and Technology."},{"key":"IJDWM.2019100102-21","unstructured":"EMC. (n.d.). [REMOVED HYPERLINK FIELD]Big Data storage [white paper]. Retrieved from https:\/\/www.emc.com\/collateral\/white-papers\/idg-bigdata-storage-wp.pdf"},{"key":"IJDWM.2019100102-22","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.03.001"},{"key":"IJDWM.2019100102-23","unstructured":"Fiedler, L., Gro\u00dfma\u00df, T., Roth, M., & Vetvik, O. J. (2016). [REMOVED HYPERLINK FIELD]Why customer analytics matter. McKinsey. Retrieved from https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/why-customer-analytics-matter"},{"key":"IJDWM.2019100102-24","author":"P. S.Foundation","year":"2018)"},{"key":"IJDWM.2019100102-25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.05.057"},{"key":"IJDWM.2019100102-26","unstructured":"Think with Google. (2016). Why cutomer analytics are the key to creating value. Retrieved from https:\/\/www.thinkwithgoogle.com\/intl\/en-gb\/marketing-resources\/data-measurement\/why-customer-analytics-are-the-key-to-creating-value\/"},{"key":"IJDWM.2019100102-27","unstructured":"Harris, D. (2018). What Is Text Analytics? We Analyze the Jargon. Software advice. Retrieved from https:\/\/www.softwareadvice.com\/resources\/what-is-text-analytics\/"},{"key":"IJDWM.2019100102-28","doi-asserted-by":"publisher","DOI":"10.2478\/v10117-011-0021-1"},{"key":"IJDWM.2019100102-29","doi-asserted-by":"publisher","DOI":"10.1057\/dbm.2009.14"},{"key":"IJDWM.2019100102-30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.12.070"},{"key":"IJDWM.2019100102-31","unstructured":"Huang, O. (2017). Applying Multinomial Naive Bayes to NLP Problems: A Practical Explanation. Medium. Retrieved from https:\/\/medium.com\/@Synced\/applying-multinomial-naive-bayes-to-nlp-problems-a-practical-explanation-4f5271768ebf"},{"key":"IJDWM.2019100102-32","unstructured":"Telus International. (2017). Customer First Magazine. Retrieved from https:\/\/www.telusinternational.com\/media\/TI-CustomersFirstMagazine-I04.pdf"},{"key":"IJDWM.2019100102-33","doi-asserted-by":"publisher","DOI":"10.1109\/HICSS.2013.645"},{"key":"IJDWM.2019100102-34","unstructured":"Kim, E. (2017). Everything You Wanted to Know about the Kernel Trick. Retrieved from http:\/\/www.eric-kim.net\/eric-kim-net\/posts\/1\/kernel_trick.html"},{"key":"IJDWM.2019100102-35","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1145\/1060745.1060797","article-title":"Opinion observer: analyzing and comparing opinions on the web.","author":"B.Liu","year":"2005","journal-title":"Proceedings of the 14th international conference on World Wide Web"},{"key":"IJDWM.2019100102-36","unstructured":"Loria, S. (2018). TextBlob. Retrieved from http:\/\/textblob.readthedocs.io\/en\/dev\/"},{"key":"IJDWM.2019100102-37","unstructured":"Maiolo, A. (2015). Comparing n-gram models. Recognize Speech. Retrieved from http:\/\/recognize-speech.com\/language-model\/n-gram-model\/comparison"},{"key":"IJDWM.2019100102-38","doi-asserted-by":"crossref","DOI":"10.5772\/6438","article-title":"A data mining & knowledge discovery process model","author":"\u00d3.Marb\u00e1n","year":"2009","journal-title":"Data mining and knowledge discovery in real life applications"},{"key":"IJDWM.2019100102-39","unstructured":"Maritz. (2018). Maximise verbatims with text analytics [white paper]. Retrieved from https:\/\/www.maritzcx.com\/blog\/wp-content\/uploads\/2014\/11\/Maritz-White-Paper-Maximise-verbatims-with-text-analytics.pdf"},{"key":"IJDWM.2019100102-40","first-page":"359","article-title":"Employing EM and pool-based active learning for text classification.","author":"A. K.McCallumzy","year":"1998","journal-title":"Proc. International Conference on Machine Learning (ICML)"},{"key":"IJDWM.2019100102-41","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2014.04.011"},{"key":"IJDWM.2019100102-42","doi-asserted-by":"publisher","DOI":"10.17077\/etd.zze4b252"},{"key":"IJDWM.2019100102-43","author":"G.Miner","year":"2012","journal-title":"Practical text mining and statistical analysis for non-structured text data applications"},{"key":"IJDWM.2019100102-44","unstructured":"Molag, T. (2017). Power BI vs Tableau. Encore Business. Retrieved from https:\/\/www.encorebusiness.com\/blog\/power-bi-vs-tableau\/"},{"key":"IJDWM.2019100102-45","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1007\/978-0-85729-320-6_91","article-title":"Data mining techniques for data cleaning","author":"K.Natarajan","year":"2010","journal-title":"Engineering Asset Lifecycle Management"},{"key":"IJDWM.2019100102-46","year":"2017)"},{"key":"IJDWM.2019100102-47","doi-asserted-by":"publisher","DOI":"10.1177\/1094670514524625"},{"key":"IJDWM.2019100102-48","unstructured":"Ott, T. (2018). What is text analytics. Software Advice. Retrieved from https:\/\/www.softwareadvice.com\/resources\/what-is-text-analytics\/"},{"key":"IJDWM.2019100102-49","article-title":"Thumbs up? Sentiment Classification using","author":"B.Pang","year":"2002","journal-title":"Machine Learning"},{"issue":"3","key":"IJDWM.2019100102-50","first-page":"39","article-title":"Understanding Customer Expectations of Service.","volume":"32","author":"A.Parasuraman","year":"1991","journal-title":"Sloan Management Review"},{"key":"IJDWM.2019100102-51","unstructured":"Project, C. (n.d.). Cran r-project. Retrieved from https:\/\/cran.r-project.org\/"},{"key":"IJDWM.2019100102-52","unstructured":"Qualtrics. (2018). What is the Net Promoter Score (NPS). Retrieved from https:\/\/www.qualtrics.com\/experience-management\/customer\/net-promoter-score\/"},{"key":"IJDWM.2019100102-53","unstructured":"Rapidminer. (2018). Rapidminer. Retrieved from https:\/\/rapidminer.com\/"},{"key":"IJDWM.2019100102-54","author":"R.\u0158eh\u016f\u0159ek","year":"2018)"},{"key":"IJDWM.2019100102-55","unstructured":"SAS. (2017). Natural Language Processing. Retrieved from https:\/\/www.sas.com\/en_us\/insights\/analytics\/what-is-natural-language-processing-nlp.html"},{"key":"IJDWM.2019100102-56","unstructured":"Smith, E. (2016). Everything you need to know about customer success. Cobloom. Retrieved from https:\/\/www.cobloom.com\/blog\/everything-you-need-to-know-about-customer-success"},{"key":"IJDWM.2019100102-57","unstructured":"Spacy.io. (n.d.). Industrial-Strength NLP. Retrieved from https:\/\/spacy.io\/"},{"key":"IJDWM.2019100102-58","doi-asserted-by":"crossref","unstructured":"Srivastava, A., & Singh, D. M. (2014). Supervised SA of product reviews using Weighted k-NN Algorithm. In 2014 11th International Conference on Information Technology.","DOI":"10.1109\/ITNG.2014.99"},{"key":"IJDWM.2019100102-59","unstructured":"Srivastava, T. (2014). Support vector machine simplified. Retrieved from https:\/\/www.analyticsvidhya.com\/blog\/2014\/10\/support-vector-machine-simplified\/"},{"issue":"10","key":"IJDWM.2019100102-60","first-page":"2319","article-title":"Sentiment Analysis of Micro blogs using Opinion Mining Classification Algorithm.","volume":"2","author":"A.Tamilselvi","year":"2013","journal-title":"International Journal of Science and Research"},{"key":"IJDWM.2019100102-61","unstructured":"Taylor, C. (2018). [REMOVED HYPERLINK FIELD]Structured vs unstructured data. Datamation. Retrieved from https:\/\/www.datamation.com\/big-data\/structured-vs-unstructured-data.html"},{"key":"IJDWM.2019100102-62","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.12.0106"},{"key":"IJDWM.2019100102-63","unstructured":"Upadhyay, P. (2017). Removing stop words NLTK python. Geeksforgeeks.com. Retrieved from https:\/\/www.geeksforgeeks.org\/removing-stop-words-nltk-python\/"},{"key":"IJDWM.2019100102-64","unstructured":"Verint. (2016). Verint Text Analytics. Retrieved from https:\/\/www.verint.com\/Assets\/resources\/resource-types\/datasheets\/text-analytics-datasheet.pdf"},{"key":"IJDWM.2019100102-65","unstructured":"Vryniotis, V. (2013). Machine Learning Tutorial: The Max Entropy Text Classifier. Datumbox. Retrieved from http:\/\/blog.datumbox.com\/machine-learning-tutorial-the-max-entropy-text-classifier\/"},{"key":"IJDWM.2019100102-66","unstructured":"Wichmann, M. (2017). The Python Wiki. Retrieved from https:\/\/wiki.python.org\/moin\/"},{"key":"IJDWM.2019100102-67","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.11.023"},{"key":"IJDWM.2019100102-68","first-page":"543","article-title":"Serendio: Simple and Practical lexicon based approach to Sentiment Analysis.","author":"V.Yadav","year":"2013)","journal-title":"Second Joint Conference on Lexical and Computational Semantics"}],"container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=237136","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T17:49:36Z","timestamp":1651859376000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJDWM.2019100102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,10,1]]},"references-count":69,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijdwm.2019100102","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"value":"1548-3924","type":"print"},{"value":"1548-3932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,1]]}}}