Abstract. Online review can help people getting more information about products. It offers a unique proposition for sentiment analysis. In this paper, a method for automatic extracting topic and it’s sentiment of hotel reviews is proposed. Our method focus on a special case which means some

Travel websites like TripAdvisor are nowadays important tools for travelers when deciding which hotels to stay in, and what restaurants and tourist attractions to visit. In this paper, we study opinion mining applied on data from travel review sites. We also describe how the results of sentiment

Recently, we can refer to user reviews in the shopping or hotel reservation sites. However, with the exponential growth of information of the Internet, it is becoming increasingly difficult for a user to read and understand all the materials from a large-scale reviews. In this paper, we propose a

Tourism is a dynamic and growing industry, with the Internet offering a multitude of new ways of conducting tourism business and promoting tourism destinations (World Tourism Organization, 2007). As the Forrester Research1 reported in 2005, in Europe 40 % of users of tourism services book their travels on the Web. However, current information technologies

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In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.

Active networks are a novel approach to network architecture in which the switches of the network perform customized computations on the messages flowing through them. This approach is motivated by both lead user applications, which perform user-driven computation at nodes within the network today, and the emergence of mobile code technologies that make dynamic network service innovation attainable. In this paper, we discuss two approaches to the realization of active networks and provide a snapshot of the current research issues and activities. Introduction – What Are Active Networks? In an active network, the routers or switches of the network perform customized computations on the messages flowing through them. For example, a user of an active network could send a “trace ” program to each router and arrange for the program to be executed when their packets are processed. Figure 1 illustrates how the routers of an IP

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

We examine the separation of ownership and control for 2,980 corporations in nine East Asian countries. In all countries, voting rights frequently exceed cash-#ow rights via pyramid structures and cross-holdings. The separation of ownership and control is most pronounced among family-controlled "rms and small "rms. More than two-thirds of "rms are controlled by a single shareholder. Managers of closely held "rms tend to be relatives of the controlling shareholder's family. Older "rms are generally family-controlled, dispelling the notion that ownership becomes dispersed over time. Finally, signi"cant corporate wealth in East Asia is concentrated among a few families. � 2000 Elsevier Science S.A. All rights reserved. JEL classixcation: G32; L22