Automating the Processing of Raw Text from a Complaint Service
Abstract
The analysis of texts from customer service is an important tool for evaluating the quality of customer service provided to consumers by suppliers. In this paper we performed an exploratory analysis of the text extracted from the responses to complaints from consumers of telecommunications services to develop a cleaning routine capable of reducing the dimensionality and noise of the data representation model. The routine presented satisfactory results, reducing the dimensionality and noise of the data representation model, contributing to the construction of more efficient classifiers.
Keywords:
Text Processing, Machine Learning, Classification Models
References
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Anatel (2020a). Painéis de Dados da Anatel. https://www.anatel.gov.br/paineis/. Acessado em: 2 junho 2021.
Anatel (2020b). Plano Estratégico Conectando a Anatel ao Futuro. [link].
Anatel (2020c). Instruções - Planilha de Avaliação Qualitatia da Resposta. [link]. Acesso em: 11 fevereiro 2021.
Anatel (2021). Panorama - Reclamações 2020. [link]. Acesso em: 11 fevereiro 2021.
Bechara, E. [Ed.] (2017). Vocabulário Ortográfico da Língua Portuguesa. 6.a ed. Rio de Janeiro (RJ): Academia Brasileira de Letras.
Faed, A., Chang, E., Saberi, M., Hussain, O. K. and Azadeh, A. (2016). Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA). Applied Soft Computing Journal, v. 47, p. 614–630.
García Adeva, J. J., Pikatza Atxa, J. M., Ubeda Carrillo, M. and Ansuategi Zengotitabengoa, E. (2014). Automatic text classification to support systematic reviews in medicine. Expert Systems with Applications, v. 41, n. 4 PART 1, p. 1498–1508.
Gomez, J. C. and Moens, M. F. (2012). PCA document reconstruction for email classification. Computational Statistics and Data Analysis, v. 56, n. 3, p. 741–751.
Guzella, T. S. and Caminhas, W. M. (2009). A review of machine learning approaches to Spam filtering. Expert Systems with Applications, v. 36, n. 7, p. 10206–10222.
Liu, Y., Wang, Y., Feng, L. and Zhu, X. (2016). Term frequency combined hybrid feature selection method for spam filtering. Pattern Analysis and Applications, v. 19, n. 2, p. 369–383.
Manning, C. D., Raghavan, P. and Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press.
Mascarenhas, Tamara A. T., Bonfante, Andreia G. (2017). Classificação de documentos de patentes usando o Doc2vec. Anais da VI Escola Regional de Informática da Sociedade Brasileira de Computação (SBC)-Regional de Mato Grosso, p. 28.
ONU (2015). Transformando Nosso Mundo: A Agenda 2030 para o Desenvolvimento Sustentável. https://nacoesunidas.org/wp-content/uploads/2015/10/agenda2030-pt-br.pdf. Acessado em: 2 junho 2021.
Ordenes, F. V., Theodoulidis, B., Burton, J., Gruber, T. and Zaki, M. (2014). Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. Journal of Service Research, v. 17, n. 3, p. 278–295.
Pedregosa et al. (2011). Scikit-learn: Machine Learning in Python. JMLR. v. 12, n. 85, p. 2825-2830.
Salton, G., Wong, A. and Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Commun. ACM, v. 18, n. 11, p. 613–620.
Silva, Fábio R. A., Bonfante, Andreia G., Martins, Claudia A. (2017). Deep Learning Aplicado à Análise de Sentimento em Linguagem Figurativa. Anais da VI Escola Regional de Informática da Sociedade Brasileira de Computação (SBC)-Regional de Mato Grosso, p. 12.
Sokolova, M. and Lapalme, G. (2007). Performance measures in classification of human communications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 4509 LNAI, p. 159–170.
Sulieman, L., Gilmore, D., French, C., et al. (2017). Classifying patient portal messages using Convolutional Neural Networks. Journal of Biomedical Informatics, v. 74, p. 59–70.
Published
2021-11-23
How to Cite
FREITAS, Maxwel de S.; ANDREÃO, Rodrigo V..
Automating the Processing of Raw Text from a Complaint Service. In: REGIONAL SCHOOL ON INFORMATICS OF RIO DE JANEIRO (ERI-RJ), 4. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 72-79.
DOI: https://doi.org/10.5753/eri-rj.2021.18777.
