Customer Feedback Analytics from Unsolicited Resources

客户反馈管理(CFM)系统已被广泛开发，以提高客户满意度和体验。然而，现有的系统在很大程度上依赖于征求反馈的渠道，如调查，而没有考虑在线社交平台，在这种平台上，客户不受私人客户-行业反馈渠道的限制，可以公开留下未经征求的反馈(社会反馈)。在这项提案中，我们试图成为利用社交网络分析和自然语言处理技术利用这些主动提供的公众反馈渠道的首批机构之一。通过与Mitacs Accelerate Support联合开展这一项目，我们的目标是：1)通过自动了解社会反馈的动态以及如何反映特定客户的需求和偏好来提高客户满意度(客户关怀)；以及2)提高客户保留率，增加商业机会，识别社区新趋势(业务支持)。我们在这项提议中的工业合作伙伴Press‘nXPress是世界上第一家也是唯一一家将社交渠道与实体接触点连接起来的加拿大公司。最重要的成果将是下一代具有社会意识的CFM，它将被越来越多的社会反馈所采用，从而增强加拿大作为CFM解决方案世界领导者之一的地位。其次，加拿大各行业将利用该项目的成果来改善他们的客户服务，屈从于留住现有客户，寻找新的潜在客户，并重新赢得以前的客户。从学术角度来看，这项提议为在公平和包容的研究和工业环境中培训高素质人员提供了坚实的基础，同时将架起社交媒体分析和客户评论分析研究的桥梁。与现有的处理社交内容的基于图形的技术不同，我们的工作将集中在使用语言建模和神经网络技术来理解社交反馈。我们将1)对服务的方面及其时间特征进行建模，2)识别以相关服务为中心的社区，3)检测新出现的基于方面的趋势。

Customer Feedback Management (CFM) systems have been extensively developed for improved client satisfaction and experience. Existing systems, however, have relied much on solicited feedback channels, such as surveys and have not considered online social platforms where the customers are not constrained in private customer-industry feedback channels and can leave unsolicited feedback publicly (social feedback). In this proposal, we seek to be among the first to leverage these unsolicited public feedback outlets using social network analysis and natural language processing techniques. Through this project jointly with Mitacs Accelerate support, we aim at 1) enhancing customer satisfaction by automatically understanding the dynamics of the social feedback and how that reflects on specific customer's needs and preferences (customer care); and 2) increasing customer retention, enhancing business opportunities and identifying emergent community trends (business support). Our industrial partner in this proposal, Press'nXPress, is the world's first and only Canadian company that will connect social channels with physical touchpoints. The foremost outcome will be the next-generation social-aware CFM that will be adopted by the growing social feedback, and therefore, enhance Canada's position as one of the world leaders in CFM solutions. Secondly, Canadian industries will leverage the outcomes of this project to improve their customer care, yielding to the retention of current customers, finding new prospective customers, and winning back former customers. From an academic perspective, while providing a solid foundation for the training of highly qualified personnel in equitable and inclusive research and industrial environment, this proposal will bridge social media analytics and customer review analysis research. Unlike the existing graph-based techniques for processing social content, our work will center on the use of language modelling and neural network technologies to make sense of social feedback. We will 1) model services' aspects and their temporal characteristics, 2) identify communities that are centred around related services, and 3) detect emergent aspect-based trends.