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.

近年来，对公司管理进行了重大的重新思考，这越来越基于客户导向原则。事实上，客户关系管理流程和系统越来越受欢迎，并且对于面对当今的业务挑战至关重要。但是，来自不同（直接和间接）渠道的大量可用的客户交流刺激需要自动语言处理技术来帮助过滤和限定此类刺激，确定优先级，促进请求的路由并减少响应时间。在这种情况下，情感分析在衡量客户满意度，跟踪消费者意见，与消费者互动以及建立客户忠诚度方面起着重要作用。本文描述的研究提出了一种基于分层注意力网络的方法来检测客户通信的情感极性。与其他现有方法不同，在进行初步培训之后，由于集成了增量学习机制，因此可以使用CRM操作员提供的反馈在系统运行期间随时间改进定义的模型。本文还描述了开发的原型以及用于训练模型的数据集，其中包括超过30.000个带注释的项目。还介绍和讨论了两个旨在测量分类器性能和验证再训练机制的实验结果。尤其是，对于支持的语言，分类器的精度要优于其他算法（0.81和0的宏平均f1-分数）。