Abstract
The main focus of this textbook thus far has been the analysis of numerical data. Text analytics, introduced in this chapter, concerns itself with understanding and examining data in word formats, which tend to be more unstructured and therefore more complex. Text analytics uses tools such as those embedded in R in order to extract meaning from large amounts of word-based data. Two methods are described in this chapter: bag-of-words and natural language processing (NLP). This chapter is focused on the bag-of-words approach. The bag-of-words approach does not attribute meaning to the sequence of words. Its applications include clustering or segmentation of documents and sentiment analysis. Natural language processing uses the order and “type” of words to infer the meaning. Hence, NLP deals more with issues such as parts of speech.
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Notes
- 1.
Tutorial link—http://www.mjdenny.com/Text_Processing_In_R.html (accessed on Dec 27, 2017).
- 2.
The dataset “icecream.csv” can be downloaded from the book’s website.
- 3.
https://raw.githubusercontent.com/sudhir-voleti/profile-script/master/sudhir%20shiny%20app%20run%20lists.txt (accessed on Dec 27, 2017).
- 4.
https://www.youtube.com/watch?v=tN6FYIOe0bs (accessed on Dec 27, 2017) Sudhir Voleti is the creator of video.
- 5.
http://wordnet.princeton.edu/ (accessed on Feb 7, 2018).
- 6.
NLTK package and documentation are available on http://www.nltk.org/ (accessed on Feb 10, 2018).
- 7.
Apache OpenNLP package and documentation are available on https://opennlp.apache.org/ (accessed on Feb 10, 2018).
References
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. Sebastopol, CA: O’Reilly Media.
Robinson, D., & Silge, J. (2017). Text mining with R: A tidy approach. Sebastopol, CA: O’Reilly Media.
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1 Electronic Supplementary Material
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Generate_Document_Word_Matrix (CPP 1 kb)
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Github shiny code (R 3 kb)
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Icecream (R 4 kb)
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Ice-cream (TXT 129 kb)
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Voleti, S. (2019). Text Analytics. In: Pochiraju, B., Seshadri, S. (eds) Essentials of Business Analytics. International Series in Operations Research & Management Science, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-68837-4_9
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DOI: https://doi.org/10.1007/978-3-319-68837-4_9
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