Text Mining Infrastructure in R

By Ingo Feinerer, Kurt Hornik, David Meyer. 2008

1,351 citations recorded by the source corpus.

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/693914b7f38c19585e35668fd626aecf62d4c5e7

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Citations in the corpus

Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science? (2012, neutral): “They are computed using the R text mining package (Meyer et al. 2008).”
The Role of the Media in Takeovers: Theory and Evidence (2015, neutral): “The software implementation uses the R packages described in Feinerer et al. (2008) and Hornik et al. (2009).”
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining (2018, background): “…field, at least comparable in importance to solar engineering.6 Text mining combines techniques in data mining, machine learning, natural language processing, information retrieval, and knowledge management, to solve the crisis of information overload in today’s world (Feldman and Sanger, 2007).”
topicmodels: An R Package for Fitting Topic Models (2011, methodology): “Package topicmodels builds on package tm (Feinerer, Hornik, and Meyer 2008; Feinerer 2011) which constitutes a framework for text mining applications within R . tm provides infrastructure for constructing a corpus, e.g., by reading in text data from PDF ﬁles, and transforming a corpus to a…”
Approaches to fully documented fisheries: practical issues and stakeholder perceptions (2015, methodology): “The responses were amalgamated, with similar words transformed to one consistent term and common stop words such as ‘a’, ‘and’ and ‘the’ removed, with the remaining text tabulated into a word-frequency using R package ‘tm’ (Feinerer et al. 2008).”
Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation (2015, background): “abstraction of the process of document manipulation (Meyer et al., 2008).”
Machine learning in medicine: a practical introduction (2019, background): “Interested readers can explore the informative tm package documentation to learn more about term-document matrices [31].”
Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study (2017, methodology): “Results were obtained with the use of R statistical software (R Core Team 2016), and R additional packages (Feinerer et al. 2008; Bouchet-Valat 2014; Wickham 2009; Fellows 2014; Hornik et al. 2009; Csardi and Nepusz 2006).”
Text Mining with R: A Tidy Approach (2017, methodology): “The package includes functions to tidy() objects (see the broom package [Robinson, cited above]) from popular text mining R pack‐ ages such as tm (Feinerer et al. 2008) and quanteda (Benoit and Nulty 2016).”
Are Financial Constraints Priced? Evidence from Textual Analysis (2018, neutral): “After extracting the MD&A section from each 10-K filing, we preprocess each MD&A (see Feinerer et al. (2008) and Li (2010)).”
stm: An R Package for Structural Topic Models (2019, methodology): “Supporting functions use a wide array of other packages including: quanteda (Benoit and Nulty 2017), tm (Meyer et al. 2008), stringr (Wickham 2015), SnowballC (Bouchet-Valat 2014), , igraph (Csardi and Nepusz 2006), huge (Zhao et al.”