Text Mining: Predictive Methods for Analyzing Unstructured Information

By S. Weiss, N. Indurkhya, Tong Zhang, Fred J. Damerau. 2004

698 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2e03533c886ce498e47debbd158bc1f936afd73f

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In Information Systems

Citations in the corpus

An approach to discovering new technology opportunities: Keyword-based patent map approach (2009, methodology): “The text document can then be characterized according to the keywords extracted through the TM algorithm (Weiss et al., 2005).”
Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century (2021, methodology): “This study uses a systematic review approach [35] and benefits from social network analysis [36] and text-mining approaches [37].”
Advanced analytics: opportunities and challenges (2009, background): “Probably more than 90 percent of a company’s data are never being tapped or looked at: letters from customers, email, correspondence, recording of phone calls with customers, contracts, technical documentation, patents, and so on (Weiss et al., 2005).”
Recommender systems based on user reviews: the state of the art (2015, methodology): “They first apply text mining tools such as a text-miner software kit and rule induction kit (Weiss et al. 2005) to select and classify review sentences into three categories: good, bad, and quality (that refers to the quality of the opinion).”
The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (2007, background): “Identifying and ranking spatial references by semantically analyzing textual data is a subset of the more general problem of named entity recognition, which locates and interprets phrasal units such as the names of people, organizations and places (Cowie and Lehnert 1996; Weiss et al. 2005).”
Management Support with Structured and Unstructured Data—An Integrated Business Intelligence Framework (2008, background): “Required are functions for identifying single information objects (“information retrieval”), and for the navigation in large knowledge bases (Weiss et al., 2005; Maier, 2004).”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, methodology): “For all of these reasons, in order to analyze the information coming from social networks, we exploit text mining techniques [17], which employ methods from the ﬁelds of 1524-9050 © 2015 IEEE.”
Text Mining Infrastructure in R (2008, background): “Classical applications in text mining (Weiss et al. 2004) come from the data mining community, like document clustering (Zhao and Karypis 2005b,a; Boley 1998; Boley et al.”