The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A cross-collection mixture model for comparative text mining
Abstract
In this paper, we define and study a novel text mining prob-lem, which we refer to as comparative text mining. Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differ-ences of these collections along each common theme. This general problem subsumes many interesting applications, in-cluding business intelligence, summarizing reviews of similar products, and comparing different opinions about a common topic. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously per-forms cross-collection clustering and within-collection clus-tering, and can be applied to an arbitrary set of compara-ble text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model. 1.
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Cites
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“They extend the information bottleneck approach [ 12 ] to discover common clusters across dierent collections.”
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Cited by
Comparing Twitter and Traditional Media Using Topic Models
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-20161-5_34 · Cited by 1,214
Topic sentiment mixture: modeling facets and opinions in weblogs
Mei, Q., Ling, X., Wondra, M., Su, H., & Zhai, C. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242596 · Cited by 801
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Ranking-based clustering of heterogeneous information networks with star network schema
Sun, Y., Yu, Y., & Han, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557107 · Cited by 513
Identifying comparative sentences in text documents
Jindal, N., & Liu, B. (2006) · Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1148170.1148215 · Cited by 446
Metadata
- Authors
- Zhai, C., Velivelli, A., & Yu, B.
- Venue
- Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2004-08-22
- Pages
- 743-748
- OpenAlex
- W2112247328
- Cited by
- 272
- References
- 12
- FWCI
- 6.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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