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based spam topic detection strategy through keyword extraction. In particular, spam topic is detected by using the topic model of multiple features with the keywords of clues, which integrate the corresponding feature of News, BBS and Blog. We get the min cost of 0.282 through TDT4 evaluating corpus and the satisfaction of
Keyword extraction is an important application in the area of information technology. Automatic keyword extraction can help people know what is the article primarily talking about without reading the long passage carefully. This paper mainly introduced a keyword extraction algorithm using pagerank on Synonym. Firstly
The popularity of blogs (as part of online social networking services) has grown dramatically in the last decade. Guided by ethnographic research of these online communities, we have designed a graphical interface for users' exploration and navigation of large scale blog network. In our design, we use the keyword
interest areas coinciding with the related book categories. This paper suggests that bloggerspsila interests can be known through extracting keywords from blog entry titles and using book classification schemes. Because there were instances in which the keywords alone did not provide adequate information, the Naver (Korean
The goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog feed retrieval procedure. Multilingual queries for retrieving blog feeds are created from Wikipedia entries. Finally, we
Writing and browsing education blogs has become one of the important methods of e-learning. Learners can search the interesting resources from these education blogs. However, the traditional blog search only provides keyword-based matching, lacking automatic extraction of learner interests and further interest-related
This paper proposes a system for finding a userpsilas interests on the Internet. It is based on his browsing behaviors and the contents of his visited pages. The system has two features. One is building userpsilas browsing interests implicitly, multiple keyword vectors, one per interest. The other is that it can
to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases
videos, we can only use a title. If there are tags - significant keywords of that multimedia, we can use tag information to search. Tag is a keyword of text, blog post, or multimedia. Users have already recognized about the value and importance of tags but only a few users are using tags. They might be annoying to add tags
A new method to compute the similarity of two blog posts is proposed in this paper. This method mainly has two parts including keywords extraction and semantic similarity measurement. During keywords extraction part, the method utilizes particular post features to extract keywords from one blog post with the aim to
events. And a huge resource of text-based emotion can be found from the World Wide Web nowadays. This paper reports a study to investigate the effectiveness of using SVM (Support Vector Machine) on linguistic features considering emotion keywords and negative words, and classify a collection of blog posts sentences tagged
concerns from several viewpoints such as temporal, geographical, and a network of blog sites. The system also facilitates users to browse multilingual keywords using Wikipedia, and the system facilitates users to find spam blogs. An overview of the CLCA architecture and the system are described.
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