Cool Blog Identi?cation Using Topic-Based Models

Sriphaew, Kritsada; Takamura, Hiroya; Okumura, Manabu

doi:10.1109/wiiat.2008.401

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Using this information and the resulting URLs the crawler is able to find websites matching the seed topics; this procedure is useful in order to exclude irrelevant websites and content. Further information about crawlers and especially blog crawlers can be found in the research work by (Hurst & Maykov, 2009), (Sriphaew et al, 2008), (Yu, F. et al, 2008), (Belsare & Joshi, 2006), and (Chakrabarti et al, 1999).…”

Section: Extraction Phasementioning

confidence: 99%

“…Thus, some authors deal with special requirements which a crawler has to meet in the context of searching for weblogs (Hurst & Maykov, 2009;S. 1615;Sriphaew, Takamura, & Okumura, 2008;Yu, F., Zheng, D., Zhao, T., & Cheng, X., 2008). Once a blog is identified, (Attardi & Simi, 2006) describe the possibilities how analysts are able to detect the individual posts of the blog.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Process Model for Content Extraction from Weblogs

Schieber¹,

Hilbert²

2014

International Journal of Intelligent Information Technologies

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This paper develops and evaluates a BPMN-based process model which identifies and extracts blog content from the web and stores its textual data in a data warehouse for further analyses. Depending on the characteristics of the technologies used to create the weblogs, the process has to perform specific tasks in order to extract blog content correctly. The paper describes three phases: extraction, transformation and loading of data in a repository specifically adapted for blog content extraction. It highlights the objectives in these phases which must be achieved to ensure the correct extraction. The authors integrate the described process in a previously developed framework for blog mining. The authors' process model closes the conceptual gap in this framework as well as the gap in current research of blog mining process models. Furthermore, it can easily be adapted for other web extraction proposals.

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Section: Extraction Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Process Model for Content Extraction from Weblogs

Schieber¹,

Hilbert²

2014

International Journal of Intelligent Information Technologies

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“…Sriphaew et al (2008) try to identify ''cool blogs,'' i.e., blogs that are worth exploring. Their approach follows a combination of credibility-like features with topic consistency, as used in blog feed search (Macdonald et al 2008b).…”

Section: Credibility In Social Mediamentioning

confidence: 99%

Credibility-inspired ranking for blog post retrieval

Weerkamp

Rijke

2012

Inf Retrieval

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Credibility of information refers to its believability or the believability of its sources. We explore the impact of credibility-inspired indicators on the task of blog post retrieval, following the intuition that more credible blog posts are preferred by searchers. Based on a previously introduced credibility framework for blogs, we define several credibility indicators, and divide them into post-level (e.g., spelling, timeliness, document length) and blog-level (e.g., regularity, expertise, comments) indicators. The retrieval task at hand is precision-oriented, and we hypothesize that the use of credibility-inspired indicators will positively impact precision. We propose to use ideas from the credibility framework in a reranking approach to the blog post retrieval problem: We introduce two simple ways of reranking the top n of an initial run. The first approach, Credibility-inspired reranking, simply reranks the top n of a baseline based on the credibility-inspired score. The second approach, Combined reranking, multiplies the credibility-inspired score of the top n results by their retrieval score, and reranks based on this score. Results show that Credibility-inspired reranking leads to larger improvements over the baseline than Combined reranking, but both approaches are capable of improving over an already strong baseline. For Credibility-inspired reranking the best performance is achieved using a combination of all post-level indicators. Combined reranking works best using the postlevel indicators combined with comments and pronouns. The blog-level indicators expertise, regularity, and coherence do not contribute positively to the performance, although analysis shows that they can be useful for certain topics. Additional analysis shows that a relative small value of n (15-25) leads to the best results, and that posts that 123Inf Retrieval (2012) 15:243-277 DOI 10.1007 move up the ranking due to the integration of reranking based on credibility-inspired indicators do indeed appear to be more credible than the ones that go down.

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“…From identified subjective sentences, opinion expressions and bag of word features are extracted. Cool blog identification (Sriphaew et al, 2008) was proposed using topic based models. Blogs with good and valued contents are called as cool blogs.…”

Section: Spam Filteringmentioning

confidence: 99%

An Improved Approach for Topic Ontology Based Categorization of Blogs Using Support Vector Machine

Antonios¹

2012

Journal of Computer Science

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Problem statement: Information search, collection and categorization from the blogosphere are still one of the important issues to be resolved. Mainly, the blogs assist the variety of interesting and useful information. Because of its increasing growth, blogs can not be categorized effectively. Therefore it is difficult to find relevant topics from the blogs. Hence blogs need to be categorized topically to make easy for readers. Approach: Blog contents are associated with a set of predefined topic ontology keywords. This study proposes categorization of blogs to facilitate easy identification of user expected topic from the massive collection of blogs. Tags, page contents were collected as inputs from the blogs and the blogs were categorized using Support Vector Machine (SVM) algorithm. Most frequent occurrences of topic ontological keywords are used to train the classifier. This approach has effectively improved blog categorization process using SVM. Results: The performance was evaluated for precision and recall for blog categorization based on topic ontology using SVM with Naive Bayes algorithm. It was proved that topic ontology assisted SVM improves the classification accuracy than Naïve Bayes algorithm. Conclusion: This study has effectively improved the classification of blogs based on topic ontology assisted SVM. Experiments showed the effectiveness of the blog categorization

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Cool Blog Identi?cation Using Topic-Based Models

Cited by 9 publications

References 3 publications

Process Model for Content Extraction from Weblogs

Process Model for Content Extraction from Weblogs

Credibility-inspired ranking for blog post retrieval

An Improved Approach for Topic Ontology Based Categorization of Blogs Using Support Vector Machine

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