Abstract
An automatic methodology is proposed for extracting valuable information from customer experience and comments after they purchase products, including the prediction of negative opinions from written phrases and the estimation of potential savings from attending unsatisfied customers. The complete methodology consists in natural language pre-processing, the calculation of the Term Frequency - Inverse Document Frequency (TF-IDF) index, the construction of binary classification models for negative opinions, and the estimation of the potential savings from using the knowledge extracted by the predictive models. The function for estimating the savings makes use of soft measures, based on fuzzy possibility functions and the estimation of client churning (given that they are unsatisfied with the delivery). The prediction algorithms for negative emotions are shown to achieve good results when tested with out-of-sample comments coming from social networks, and the complete methodology obtains significant savings when applied for e-commerce customer support.
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Franco, C., Cadavid, D. (2022). Estimating Fuzzy Possibility Functions for E-Commerce Decision Support. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_13
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