Impact of Population Aging on Japanese International Travel

In this paper we forecast Japanese international travel to 2025. In addition to the usual economic variables, our model also captures both population aging and cohort effects on Japanese travel abroad. We predict the number of future Japanese overseas trips for males and females separately by five-year age groups and in five-year increments. We conclude that the Japanese will continue to travel abroad in increasing numbers but population aging will dramatically slow overall future Japanese overseas travel. While the number of “senior” travelers is predicted to increase sharply, we foresee fewer overseas trips taken by Japanese, especially among women, in the 20s and early 30s age groups. Finally, we examine the responses of the industry and the public sector in Japan to implications of a rapidly aging population on future international travel

Impact of Population Aging on Japanese International Travel to 2025

In this paper we forecast Japanese international travel to 2025. In addition, to the usual economic variables, our model also captured both populations again and cohort effects on Japanese travel abroad. We predict the number of future Japanese overseas trips for males and females separately by five-year age groups and in five-year increments. We conclude that the Japanese will continue to travel abroad in increasing numbers but population aging will dramatically slow overall future Japanese overseas travel. While the number of "senior" travelers is predicted to increase sharply, we foresee fewer overseas trips taken by Japanese, especially among women, in the 20s and early 30s age groups. Finally, we examine the responses of the industry and the public sector in Japan to implications of a rapidly aging population on future international travel.

Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing (RANLP) 201

Visual intelligence for online communities : commonsense image retrieval by query expansion

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (leaves 65-67).This thesis explores three weaknesses of keyword-based image retrieval through the design and implementation of an actual image retrieval system. The first weakness is the requirement of heavy manual annotation of keywords for images. We investigate this weakness by aggregating the annotations of an entire community of users to alleviate the annotation requirements on the individual user. The second weakness is the hit-or-miss nature of exact keyword matching used in many existing image retrieval systems. We explore this weakness by using linguistics tools (WordNet and the OpenMind Commonsense database) to locate image keywords in a semantic network of interrelated concepts so that retrieval by keywords is automatically expanded semantically to avoid the hit-or-miss problem. Such semantic query expansion further alleviates the requirement for exhaustive manual annotation. The third weakness of keyword-based image retrieval systems is the lack of support for retrieval by subjective content. We investigate this weakness by creating a mechanism to allow users to annotate images by their subjective emotional content and subsequently to retrieve images by these emotions. This thesis is primarily an exploration of different keyword-based image retrieval techniques in a real image retrieval system. The design of the system is grounded in past research that sheds light onto how people actually encounter the task of describing images with words for future retrieval. The image retrieval system's front-end and back- end are fully integrated with the Treehouse Global Studio online community - an online environment with a suite of media design tools and database storage of media files and metadata.(cont.) The focus of the thesis is on exploring new user scenarios for keyword-based image retrieval rather than quantitative assessment of retrieval effectiveness. Traditional information retrieval evaluation metrics are discussed but not pursued. The user scenarios for our image retrieval system are analyzed qualitatively in terms of system design and how they facilitate the overall retrieval experience.James Jian Dai.S.M