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
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
My portal viewer: integration system based on user preferences for news web sites
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems 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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Using morphological and syntactic structures for Chinese opinion analysis
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Design of impression scales for assessing impressions of news articles
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
Topic-based Bengali opinion summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A bipartite graph model and mutually reinforcing analysis for review sites
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
The 5w structure for sentiment summarization-visualization-tracking
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Sentiment analysis: what is the end user's requirement?
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Words-of-wisdom search system based on user's desired sentiment
International Journal of Business Intelligence and Data Mining
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We have developed a news portal site called Fair News Reader (FNR) that recommends news articles with different sentiments for a user in each of the topics in which the user is interested. FNR can detect various sentiments of news articles, and determine the sentimetal preferences of a user based on the sentiments of previously read articles by the user. While there are many news portal sites on the Web, such as GoogleNews, Yahoo!, and MSN News, they can not recommend and present news articles based on the sentiments they are likely to create since they simply select articles based on whether they contain user-specified keywords. FNR collects and recommends news articles based on the topics in which the user is interested and the sentiments the articles are likely to create. Eight of the sentiments each article is likely to create are represented by an "article vector" with four elements. Each element corresponds to a measure consisting of two symmetrical sentiments. The sentiments of the articles previously read with respect to a topic are then extracted and represented as a "user vector". Finally, based on a comparison between the user and article vectors in each topic, FNR recommends articles that have symmetric sentiments against the sentiments of read articles by the user for fair reading about the topic. Evaluation of FNR using two experiments showed that the user vectors can be determined by FNR based on the sentiments of the read articles about a topic and that it can provide a unique interface with categories containing the recommended articles.