A Brief Survey of Text Mining

By Andreas Hotho, Andreas Nürnberger, Gerhard Paass. Ldv Forum, 2005

1,025 citations recorded by the source corpus.

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/96430cc91ed91fd2d4042fa6fcb7ecf4005d77a7

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Citations in the corpus

Explaining Data-Driven Document Classifications (2014, methodology): “Often a weighting scheme is applied to the frequencies, wher e t weights reflect the importance of the word for the specific application (Hotho et al. 2005).”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “A single SVM can only separate two classes, a positive class and a negative class [67].”
Real-Time Detection of Traffic From Twitter Stream Analysis (2015, background): “See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. data mining, machine learning, statistics, and Natural Language Processing (NLP) to extract meaningful information [18].”
A Review of Machine Learning Algorithms for Text-Documents Classification (2010, background): “Information retrieval in the broader sense deals with the entire range of information processing, from data retrieval to knowledge retrieval [7].”