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.
Highlights
Customer Relationship Management (CRM) is a technologybased approach aimed at improving the management of the company’s interaction with its customers
After a customizable timeframe, the classifier is retrained with samples from the feedback repository plus random samples selected from the original training set
This paper presents an approach based on Hierarchical Attention Networks for detecting the sentiment polarity of customer requests
Summary
Customer Relationship Management (CRM) is a technologybased approach aimed at improving the management of the company’s interaction with its customers. Alternative approaches based on Natural Language Processing (NLP) can help provide an automated way of generating insights from such a large amount of texts This includes identifying the language of the text, the intention of the customer, the products and services involved, as well as the customer sentiment. The proposed approach is based on text categorization: text messages containing customer requests (from multiple channels) are classified as positive, negative or neutral. The paper is organized as follows: in section 2 the related work on SA and customer opinion mining is summarized and the work is contextualized in the relevant literature; in section 3 the proposed approach is presented, which includes the description of the preprocessing and classification steps, the adopted model and the defined retraining algorithm for model improvement. The last section summarizes the conclusions and outlines the ongoing work
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