Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Tracking and summarizing news on a daily basis with Columbia's Newsblaster
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Extracting content structure for web pages based on visual representation
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
Proposal of impression mining from news articles
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
A credibility analyzing method of geographical objects from digital maps
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
A method of analyzing credibility based on LOD control of digital maps
Proceedings of the 3rd workshop on Information credibility on the web
An information theoretic approach to sentiment polarity classification
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
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We have developed a visualizing news system that shows the trend of the news site on the Web for credibility. If users know the trend of the news site, users can evaluate the credibility of each news topic. This system detects and uses sentiments of each news article to resolve the trend of Web site. The trend of Web sites are extracted as average sentiments of the news articles that were written concerning a topic in each Web site. The sentiments of news articles are represented by four values calculated in four sentiment scales: "Bright ⇔ Dark", "Acceptance ⇔ Rejection", "Relaxation ⇔ Strain", and "Anger ⇔ Fear". The sentiment values of news articles are calculated using the sentiment dictionary that was constructed by our previously proposed method. If a user enters one or more topic keywords, our proposed system extracts the news articles that include the keywords from each predetermined news sites. Our system also calculates the sentiment values of the news articles and their average value in each sentiment from each news Web site. The system then generates a bar graph from the four average values in each news Web site and attaches all the bar graphs on the world map using Google Map API. We call the map a sentiment map in this paper. The sentiment map helps users intuitively and efficiently understand trends among multiple Web sites concerning a given topic. In this paper, we describe how to create a sentiment map and explain how we evaluated our proposed system through several experiments.