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Published in:
2011 | OriginalPaper | Chapter
Automatic Classification of Link Polarity in Blog Entries
Authors : Aya Ishino, Hidetsugu Nanba, Toshiyuki Takezawa
Published in: Information Retrieval Technology
Publisher: Springer Berlin Heidelberg
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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.