Yonnim Lee

Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media

Abstract IT vendors routinely use social media such as YouTube not only to disseminate their IT product information, but also to acquire customer input efficiently as part of their market research strategies. Customer responses that appear in social media, however, are typically unstructured; thus, a fairly large data set is needed for meaningful analysis. Although identifying customers’ value structures and attitudes may be useful for developing targeted or niche markets, the unstructured and volume-heavy nature of customer data prohibits efficient and economical extraction of such information. Automatic extraction of customer information would be valuable in determining value structure and strength. This paper proposes an intelligent method of estimating causality between user profiles, value structures, and attitudes based on the replies and published content managed by open social network systems such as YouTube. To show the feasibility of the idea proposed in this paper, information richness and agility are used as underlying concepts to create performance measures based on media/information richness theory. The resulting deep sentiment analysis proves to be superior to legacy sentiment analysis tools for estimation of causality among the focal parameters.

Acoustic sensor based recognition of human activity in everyday life for smart home services

A novel activity recognition method is proposed based on acoustic information acquired from microphones in an unobtrusive and privacy-preserving manner. Behavior detection mechanisms may be useful in context-aware domains in everyday life, but they may be inaccurate, and privacy violation is a concern. For example, vision-based behavior detection using cameras is difficult to apply in a private space such as a home, and inaccuracies in identifying user behaviors reduce acceptance of the technology. In addition, activity recognition using wearable sensors is very uncomfortable and costly to apply for commercial purposes. In this study, an acoustic information-based behavior detection algorithm is proposed for use in private spaces. This system classifies human activities using acoustic information. It combines strategies of elimination and similarity and establishes new rules. The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression.

A Galois lattice approach to a context-aware privacy negotiation service

Online privacy has consistently been a major concern for customers that has grown commensurately with the growth of e-services. Service providers have responded by making their privacy policies clearer for customers; however, most providers use legacy systems that are unable to actually change and adapt to users concerns, which can lead to fewer customers using the system. Hence, the purpose of this paper is to propose a context-aware privacy policy negotiation service. To do so, we adopt a Galois lattice theory to generate policy concepts embedded in e-services. Based on the Galois lattice, we develop a process for generating privacy policy rules. To show the feasibility of the ideas proposed in this paper, we perform a simulation test with two different online auction sites as an illustrative case in terms of two metrics: the number of rules generated and success throughput. Desirable features in applying the Galois lattice approach to context-aware privacy policy negotiation service are discussed.

Lattice Based Privacy Negotiation Rule Generation for Context-Aware Service

Online privacy has consistently been a major concern for customers, growing commensurately with the growth of online commerce. Individuals often have serious concerns that their online activities are being monitored, which can prevent them from using online services. With this concern in view, service providers have started making their privacy policies more clear to customers. However, legacy systems often lack flexibility and the inability to adapt to users interests, which are often the main reasons for inappropriate agreement. Negotiation between the service provider and user can be a possible solution to reach an agreement which seems appealing and profitable to both parties. In this paper we have developed a negotiation mechanism using the concept lattice approach. Using concept lattices in a privacy policy makes it flexible and allows both the parties to sacrifice a few interests for mutual benefits and appropriate agreement.

Criteria for Measuring Information Privacy in Context-Aware Computing Environments

A number of context-aware systems have been developed over the last decade. However, according to the recent studies, concerns of overflowing context information have been increased. Better understanding and classification of information privacy concepts under context-aware computing environments are highly needed. Hence, the purpose of this paper is to develop the measurement criteria for information privacy in context-aware computing environments and then prioritize the criteria. Overall technology characteristics are considered to establish a mutually exclusive set of criteria which measure information privacy in context-aware computing environments in a unique and complete manner. To do so, Delphi method was adopted to obtain the reliable opinion from the experts in information privacy, as well as context-aware systems. Based on this analysis, the panels emphasize context-awareness, tracking, recording, sensors, infrastructure, and hence, they are added to our evaluation model as main criteria.

An index-based privacy preserving service trigger in context-aware computing environments

In a context-aware environment, privacy concerns of a user should be certainly considered in order to raise service receptiveness. The requirement for providing suitable privacy preserving service after forecast of privacy concern is increasing. An especially important factor is to provide a privacy protection level which is suitable to the privacy concern level of the user, rather than increasing the privacy protection level unconditionally. For this reason, it is necessary for each service provider to obtain the current privacy concern of the user so as to optimize the privacy preserving level. Existing measurements of privacy concern are carried out through a sample survey form that is composed of uncertain pre-forecast and untimely post-analysis. So, accurately measuring the privacy concern in a personalized manner is limited. This study is aimed at recognizing user regarding privacy concern, and then proposing measures for the service provider to supply a service which has a proper level of privacy preservation, based on the recognized results. For this purpose, this study developed a method of forecasting the privacy concern based on an index model of privacy concern and also an approach method of triggering the privacy preserving service.

Context-aware enhancement of personalization services: A method of power optimization

Abstract Context-aware applications, which consist of a sensor system, a reasoning system and service artifacts such as mobile devices, kiosks and robots, require data from the sensors to be queried on a continuous basis. The smaller the sensing interval and the greater the amount of service time, the more accurate the service, but the more energy is consumed. Thus, use of context-aware applications always involves a trade-off. In this paper, we propose an automatic method of optimizing the level of personalization involving the sensing cycle and service time of a personalized application. The method proposes a quadratic form of total cost curve which demonstrated that the minimum identified value is always the global optimum. This eliminates the necessity of an exhaustive search for the minimum value for all levels of personalization.

A Structured Methodology of Scenario Generation and System Analysis for Ubiquitous Smart Space Development

Ubiquitous smart space (USS) has been regarded as a promising extension of ubiquitous services, and it is currently the subject of world-wide development. In one USS development methodology, scenario development is performed before system analysis and design. However, even though many redundant elements can be found between scenarios and system analysis results, developers have not been taking any structural approaches to join them together for more consistency and eventually higher productivity. Hence, the aim of this paper is to propose a methodology to increase the consistency in the early steps of USS development. To do so, scenario and requirement analysis are integrated in a structured manner.