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This is based on natural language processing (NLP) for text data preparation and on machine learning (ML) for clustering model identification. Several combinations for preprocessing, vectorisation, dimensionality reduction and clustering techniques, were investigated. The case study refers to a Brazilian electric energy company, with a data set of failed customer queries, that is, not met by the company for any reason. They correspond to about 30% (4,044 queries) of the original data set. The best identified intent model employed stemming for preprocessing, word frequency analysis for vectorisation, latent Dirichlet allocation (LDA) for dimensionality reduction, and mini-batch [Formula: see text]-means for clustering. This system was able to allocate 62% of the failed queries in one of the seven found intents. For instance, this new labelled data can be used for the training of NLP-based chatbots contributing to a greater generalisation capacity, and ultimately, to increase customer satisfaction. <\/jats:p>","DOI":"10.1142\/s0219649221500374","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T08:05:46Z","timestamp":1624349146000},"page":"2150037","source":"Crossref","is-referenced-by-count":0,"title":["Intent Identification in Unattended Customer Queries Using an Unsupervised Approach"],"prefix":"10.1142","volume":"20","author":[{"given":"Hugo D.","family":"Rebelo","sequence":"first","affiliation":[{"name":"Radix \u2013 Engineering and Software, Passeio Corporate, R.\u00a0do Passeio, 38, Tower 2, Centro, Rio de Janeiro 20021-290, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas A.\u00a0F.","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Radix \u2013 Engineering and Software, R.\u00a0Santa Rita Dur\u00e3o, 444, Funcion\u00e1rios, Belo Horizonte 30140-110, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustavo M.","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, School of Engineering, Federal University of Minas Gerais, Av. 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