Emotional labor in text-based chat environments will be widespread in the near future. It is difficult for novice workers to make smooth conversation with an angry customer in a customer-support work- ing environment, one of the stressful emotional labors. In this pa- per, we propose a method to suppress the customer's anger based on a communication agent introduced into customer-support work in order to facilitate smooth communication. The agent first estab- lishes a positive relationship with the customer, and then the atti- tude changes to be sympathetic to the customer-support worker in order for the customer to follow sympathy and reduce anger. From the experimental evaluation, it is found that there is the possibil- ity of suppressing the participants' anger by this method, but the anger itself is at a higher level than in the common environment without the agent.

Brand advertising is a type of advertising that aims at increasing the awareness of companies or products. This type of advertising is well studied in economic, marketing, and psychological literature; however, there are no studies in the area of computational advertising because the effect of such advertising is difficult to observe. In this study, we consider a real-time biding strategy for brand advertising. Here, our objective to maximizes the total number of users who remember the advertisement, averaged over the time. For this objective, we first introduce a new objective function that captures the cognitive psychological properties of memory retention, and can be optimized efficiently in the online setting (i.e., it is a monotone submodular function). Then, we propose an algorithm for the bid optimization problem with the proposed objective function under the second price mechanism by reducing the problem to the online knapsack constrained monotone submodular maximization problem. We evaluated the proposed objective function and the algorithm in a real-world data collected from our system and a questionnaire survey. We observed that our objective function is reasonable in real-world setting, and the proposed algorithm outperformed the baseline online algorithms.

The objective of this paper is to develop an intelligent computer system with some deductive capabilities to conceptually cluster, match and rank pages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The conceptual fuzzy set (CFS) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and links (here defined as "Concept"). The selected query doesn't need to match the decision criteria exactly, which gives the system a more human-like behavior. The model can be used for intelligent information and knowledge retrieval through Web-connectivity-based clustering.