Abstract
For improving the cooperative detection ability of hot topics, this paper analyzes web topic spreading features and proposes a hot topic detection model based on opinion analysis for web forums in distributed environment (named TOAM). The model not only evaluates the topic opinion impacts of the single web forum, but also cooperatively schedules the opinion information of hot topics among different network domains. TOAM monitors the topics opinion of each web forum and generates the hot topics of local network domain periodically. Through scheduling the local hot topics between different network domains, the model effectively improves the ability of topic spreading analysis and optimizes the local topics information database. To validate the performance, the experiments on the data corpus about “Campus Alert Network Culture” demonstrate that TOAM has higher application validity and practicality of hot topic detection for web forums in distributed environment.
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References
Manquan, Y., Weihua, L., et al.: Research on Hierarchical Topic Detection in Topic Detection and Tracking. Journal of Computer Research And Development 43(3), 489–495 (2006)
Li, Y., Meng, X., Li, Q., Wang, L.: Hybrid Method for Automated News Content Extraction from the Web. In: proceeding of 7th International Conference on Web Information Systems Engineering, pp. 327–338 (2006)
Pang, B., Lee, L.: Vaithyanathan, Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on EMNLP, pp. 79–86 (2002)
Soo-Min, K., Eduard, H.: Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text. In: Proceedings of the ACL Workshop on Sentiment and Subjectivity in Text, pp. 1–8 (2006)
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation (2005)
Soboroff, I., Harman, D.: Overview of the TREC 2003 novelty track. In: The Twelfth Text REtrieval Conference, National Institute of Standards and Technology, pp. 38–53 (2003)
NTCIR Project. (Available via DIALOG, 2009), http://research.nii.ac.jp/ntcir/index-en.html (Cited 10 June 2009)
Ku, L.-W., Liang, Y.-T., Chen, H.-H.: Opinion extraction, summarization and tracking in news and blog Corpora. In: Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs, pp. 100–107 (2006)
Xu, R., Wong, K.-F., et al.: Learning Knowledge from Relevant Webpage for Opinion Analysis. Web Intelligence and Intelligent Agent Technology, pp. 307–313 (2008)
Stoyanov, V., Cardie, C.: Topic Identification for Fine-Grained Opinion Analysis. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 817–824 (2008)
Abbasi, A., Chen, H., Thoms, S., Fu, T.: Affect Analysis of Web Forums and Blogs Using Correlation Ensembles. IEEE Transactions on Knowledge and Data Engineering 20(9) (September 2008)
Yuen, R.W.M., Chan, T.Y.W., et al.: Morpheme-based Derivation of Bipolar Semantic Orientation of Chinese Words. In: Proceedings of the 20th International Conference on Computational Linguistics, pp. 1008–1014 (2004)
Xie, T., Sha, C., et al.: Approximate Top-k Structural Similarity Search over XML Documents. Frontiers of WWW Research and Development, 319–330 (2006)
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Hu, C., Weng, Y., Zhang, X., Xue, C. (2009). Hot Topic Detection Based on Opinion Analysis for Web Forums in Distributed Environment. In: Papadopoulos, G.A., Badica, C. (eds) Intelligent Distributed Computing III. Studies in Computational Intelligence, vol 237. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03214-1_10
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DOI: https://doi.org/10.1007/978-3-642-03214-1_10
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