The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Automatically collecting, monitoring, and mining japanese weblogs
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
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
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Towards multi-paper summarization reference information
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Automatic compilation of travel information from automatically identified travel blogs
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
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In this paper, we propose a method for classification of an author's sentiment for a linked blog (we call this sentiment link polarity), as a first step for finding authoritative blogs in the blogosphere. Generally, blogs that are linked positively from many other blogs are considered more reliable. In citing a blog entry, there are passages where the author describes his/her sentiments about a linked blog (which we call citing areas). We extract citing areas in a Japanese blog entry automatically, and then classify a link polarity using the information in the citing areas. To investigate the effectiveness of our method, we conducted experiments. For classification of link polarity, we obtained a high precision and recall than baseline methods. For the extraction of the citing areas, we obtained the same Precision and Recall as manual extraction. From our experimental results, we confirmed the effectiveness of our methods.