Web Data Mining

By S. Mankovskii, Maarten van Steen, Minos N. Garofalakis, Alan D. Fekete, C. S. Jensen, R. Snodgrass, A. Wun, Vanja Josifovski, Andrei Z. Broder, Dennis Fetterly and 46 more. Encyclopedia of Database Systems, 2009

332 citations recorded by the source corpus.

Subfields: Information Systems

View Paper: https://www.semanticscholar.org/paper/6de6a32badc6a142f6f5a51d0cb68ef8c5a76da0

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In Information Systems

Citations in the corpus

Applied Predictive Modeling (2013, methodology): “We know from the work of Liu and Rayens (2007) that LDA should outperform PLS in terms of minimizing misclassification errors.”
A review of the recent literature on rainfall thresholds for landslide occurrence (2018, methodology): “To obtain the same objective with reduced workforce, some researchers are developing methods to provide automated, fast, and timely updates by means of semantic search engines on online news (Battistini et al. 2013, 2017).”
Mining topics in documents: standing on the shoulders of big data (2014, background): “This is called the rare item problem in data mining and has been well documented in [18].”
Review spam detection (2007, background): “In this paper, we focus on customer reviews of products, which contain information of consumer opinions on the products, and are useful to both potential customers and product manufacturers [4, 8].”
A reference model for learning analytics (2012, methodology): “The collected data may be too large and/or involve many irrelevant attributes, which call for data pre-processing (also referred to as data preparation) (Liu, 2006).”
Sentiment analysis of blogs by combining lexical knowledge with text classification (2009, methodology): “For the Näıve Bayes model, we compute the conditional probability estimates using Lidstone smoothing [14] with = 1 × 10−6, i.”