Libraries used to convert incident documents into numerical vectors

Libraries used to Convert Incident Documents into Numerical Vectors
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Table 4316. Libraries used to convert incident documents into numerical vectors (Natural Language Processing (NLP)).

	spaCy	Natural Language Toolkit (NLTK)
Toolkit	NLP	NLP
Use	Simpler to use
Differences	Adopts an object-oriented approach	As a string processing library
Differences	Is more efficient when working with words, e.g. identify keywords	Performs better than spaCy when analyzing sentences [1]

Features	Many features that can be used to pre-process text data – which comes with tokenization and lemmatization features which were used to transform the words in the incident database to their canonical form. E.g the words "run", "running", and "ran" would all be reverted to "run" [2]
Typical application	For keyword analysis in this research [2]

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[1] Malhotra, A., 2018. Introduction to Libraries of NLP in Python - NLTK vs. spaCy. Retrieved from https://medium.com/@akankshamalhotra24/introduction-to-libraries-of-nlp-in-python-nltk-vs-spacy-42d7b2f128f2.
[2] Daniel Kurian, Fereshteh Sattari, Lianne Lefsrud, Yongsheng Ma, Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations, Safety Science, 130(2020), 104873.

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