An Emotional Comfort Framework for Improving User Satisfaction in E -Commerce Customer Service Chatbots (2021)

Abstract: E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. According to the survey of user studies and the real online testing, emotional comfort of customers’ negative emotions, which make up more than 5% of whole number of customer visits on AliMe, is a key point for providing considerate service. In this paper, we propose a framework to obtain proper answer to customers’ emotional questions. The framework takes emotion classification model as a core, and final answer selection is based on topic classification and text matching. Our experiments on real online systems show that the framework is very promising.

## introduction a chatbot is considered as a question answering system in which experts provide knowledge on users' behest. meanwhile, chatbots are not just question answering systems, since they can carry out a lot of tasks depending on how you design it @xcite . as chatbot has become an important solution to rapidly increasing customer service demands in recent years, many companies have recently launched their own intelligent customer service (ics) chatbots for providing customer service, such as lenovo @xcite , fujitsu @xcite , jd.com @xcite and alibaba @xcite . for customers' emotional questions, proper emotional comfort can help improve the service. this is not only applicable to customer service staffs, but also a key point of ics chatbots, while demonstrating human-like service is the ultimate goal of ics chatbots. emotional quotient (eq) has been a core competence of chatbot @xcite , and about eq, we can roughly categorize it into two key components: identifying users' emotions and giving users proper emotional responses. besides, chatbots' eq is domain-specific, since it is mainly based on emotion analyzing, and emotionanalyzing technologies are mostly domain specific. in this paper, we introduce an emotional comfort framework for the e-commerce chatbots. ecommerce customers usually complain of slow delivery, poor quality of goods and difficulty of contacting sellers, etc. traditional question answering based ics chatbots may just reply customers with some pieces of 'knowledge' such as 'how to speed up the delivery', 'how to report the quality issues of goods' and 'how to contact sellers'. without responses that are emotionally appropriate, ics robots are too 'robotic' to users. human-like empathy and appropriate emotional reply can help the users regain their confidence and move forward with a positive attitude. besides, in our framework we don't consider emotional response generation models, such as @xcite and @xcite , since we should meet the high queries-persecond (qps) needs of real online applications. figure 1 gives two simple examples for the comparison of traditional ics chatbots and emotional ics chatbots, which are without or with emotional comforts. without emotional comfort, the response appears abruptly. in our sentiment classification model and topic classification model, we combine those multiplelevel features, and prove that our model can achieve significantly improved results. emotional chatbot: the most famous emotional chatbot is xiaoice @xcite , which was designed about 6 years ago. understanding and responding to users' emotions are two dimensions of the ability of emotional chatbots. for realizing a human-like customer service chatbot, we try to understand users' emotions with an emotion classification model, and detect topics in user questions with a topic classification model. then for responding users' emotions, we design an emotional comfort framework including matching based comfort, comfort with considering both emotion and topic, and a base comfort with just considering emotion. text matching: text matching needs to capture the rich interaction structures in the matching process, and this process can be conducted between abstract features of two texts @xcite @xcite or between word embedding of two texts @xcite @xcite . in papers @xcite @xcite (the arc-i model in @xcite ), they all extract features from each of those two texts and then combine those features as the input of final logistic regression model. in papers @xcite @xcite (the arc-ii model in @xcite ), they all take the interaction matrix of two texts as input of their models, and extract features from the given interaction matrix to evaluate similarity between two texts. in our matching-based emotional comfort part, we combine a bcnn model @xcite , which is with a text interaction on abstract feature level, and a matchpyramid model @xcite , which is with a text interaction on word embedding level, to obtain an eligible performance for online service. ## framework description our proposed framework consists of two parts (figure 2 ), offline part and online part, and each of them consists of three components. with the offline part, we want to realize the ability to understand users' emotions as detailed as possible, and with the online part, we sequentially run increasingly general comfort strategies for responding users' emotions on a larger scale. offline part: 1) emotion classification model is trained with considering word-level features, ngram level features and sentence level features. we consider seven different emotions as fear, abuse, disappointed, aggrieved, anxious, anger and grateful. 2) topic classification model is trained with a same way as the emotion classification model, and we choose 35 high frequency service classes, such as 'complaints about the quality of service' and 'complaints of slow delivery', etc. 3) knowledge construction is for collecting some user questions with very specific content that needs to response emotional comforts. those specific questions are with high frequency, but they are hard to be classified into a topic or cannot get well treated with just topic-level comforts. for each question, our service experts will design a professional reply, and for each 'question-reply' pair we call it as a piece of 'knowledge'. online part: 1) knowledge-based comfort is for users with specific questions, and we use a text-matching model to match a user's question and the high-frequent questions in collected pieces of knowledge. if we can get a prepared question, which has the biggest similarity with the given user's question and also the similarity value is bigger than a particular threshold, the corresponding reply will be taken as the emotional comfort result to this user. 2) emotion & topic comfort means the comfort based on both users' emotions and the topics of users' questions. 3) emotion-level comfort is a backup component to the emotion & topic comfort, since we cannot list all topics. so for other emotional queries without listed topics, we use this component to reply a general emotional response. figure 3 gives examples of online emotional comforts. (a) shows an emotion-level comfort example. this user just complains, without any topic or any reason, so we can just give this user a very general comfort. (b) shows a comfort considering both emotions and topics. this user complains about service, so we can pointedly give a comfort about service. (c) shows a user's complain about bad robot service, and for this kind of questions with very specific content, we utilize knowledgebased matching models to give proper responses. ## conclusion in this paper, we focus on an emotional comfort framework in e-commerce chatbots, and the experiments show such a framework can effectively improve user satisfaction. about the future work, we will consider more emotions in this framework. besides, we will automatically evaluate users' satisfaction with technologies on emotion analysis and sequence labeling.