Human-centric information technology and applications towards web 3.0

Abstract

Nowadays, many people can participate in intelligence production through various processes, such as publications, distributions, and services. In order to recognize, interpret, and process opinions and sentiments over the web, sentiment analysis is emerging as an important issue in human-centric information technology. Paradigms of sentiment analysis such as machineunderstandable web and human-centric web offer a promising and potential solution to mining and analyzing the very large and varied web data. To improve computers’ understanding of human-centric information, relationship analysis between persons with shared common sense information known to everyone is necessary. The seven papers in this Special Issue address some of the challenges inherent in this. The first paper “Human Likeness: Cognitive and Affective Factors Affecting Adoption of Robot-Assisted Learning Systems” by Hosun Yoo, Ohbyung Kwon, and Namyeon Lee proposes an integrated theoretical model, which explains the effect of human likeness of robots on user’s propensity to adopt robot-assisted learning systems. The human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and parasocial relationships. To validate the proposed model, a robot-assisted learning prototype was utilized and survey data were collected from general users. The resulting data were empirically tested, and the test results were provided and analyzed. The second paper entitled “Predicting Personality Traits Related to Consumer Behavior using SNS Analysis” by Baik et al. proposes a method for predicting the four personality traits – (1) Extroversion, (2) Public Self-Consciousness, (3) Desire for Uniqueness, and (4) Self-Esteem – that correlate with buying behaviors in the recent consumer behavior discipline. They also propose another method to analyze user behaviors in a social network service by using user behavior matrix, friendship analysis, and route analysis. In the third paper entitled “An Efficient Scheme for Automatic Web Pages Categorization using Support Vector Machine”, Vinod Kumar Bhalla and Neeraj Kumar proposes a support vector machine (SVM)-based web page categorization, which is based on identification of specific and relevant features of the web pages. They also developed a feature extraction tool based on the HTML-DOM of web page. They evaluated the proposed scheme using SVM kernel as a classification tool in combination with feature extraction and statistical analysis. The fourth paper entitled “Sentiment Classification Technology Based on Markov Logic Networks” by Hui He, Zhigang Li, Chongchong Yao, and Weizhe Zhang presents a crossdomain multi-task text sentiment classification method based on Markov Logic Networks. Through many-to-one knowledge transfer, labeled text sentiment classification knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved.