Sentiment analysis for customer relationship management: an incremental learning approach
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Sentiment analysis for customer relationship management: an incremental learning approach Nicola Capuano 1
&
Luca Greco 2 & Pierluigi Ritrovato 2 & Mario Vento 2
Accepted: 25 September 2020 # The Author(s) 2020
Abstract In recent years there has been a significant rethinking of corporate management, which is increasingly based on customer orientation principles. As a matter of fact, customer relationship management processes and systems are ever more popular and crucial to facing today’s business challenges. However, the large number of available customer communication stimuli coming from different (direct and indirect) channels, require automatic language processing techniques to help filter and qualify such stimuli, determine priorities, facilitate the routing of requests and reduce the response times. In this scenario, sentiment analysis plays an important role in measuring customer satisfaction, tracking consumer opinion, interacting with consumers and building customer loyalty. The research described in this paper proposes an approach based on Hierarchical Attention Networks for detecting the sentiment polarity of customer communications. Unlike other existing approaches, after initial training, the defined model can improve over time during system operation using the feedback provided by CRM operators thanks to an integrated incremental learning mechanism. The paper also describes the developed prototype as well as the dataset used for training the model which includes over 30.000 annotated items. The results of two experiments aimed at measuring classifier performance and validating the retraining mechanism are also presented and discussed. In particular, the classifier accuracy turned out to be better than that of other algorithms for the supported languages (macro-averaged f1-score of 0.89 and 0.79 for Italian and English respectively) and the retraining mechanism was able to improve the classification accuracy on new samples without degrading the overall system performance. Keywords Customer relationship management . Hierarchical attention networks . Machine learning . Natural language processing . Sentiment analysis
1 Introduction
* Nicola Capuano [email protected] Luca Greco [email protected] Pierluigi Ritrovato [email protected] Mario Vento [email protected] 1
University of Basilicata, School of Engineering, Viale dell’Ateneo Lucano, 10, 85100 Potenza, Italy
2
Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
Customer Relationship Management (CRM) is a technologybased approach aimed at improving the management of the company’s interaction with its customers. It consists of techniques and tools for analyzing, acquiring and processing customer data with the intention of driving business decisions and building customer loyalty [1]. Companies and customers can interact in a variety of ways including e-mails, instant messaging, websites, apps, etc. In addition to direct communication channels
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