Evaluating Techniques for Pre-Processing of Unstructured Text For Text Classification
DOI:
https://doi.org/10.26438/ijcse/v6i8.151160Keywords:
Tokenisation, stemming, parts of speech tagging, document representation, vector space modelAbstract
The availability of digital information over the internet can be analyzed for knowledge discovery and intelligent decision making. Text categorization is an important and extensively studied problem in machine learning. Text classification or grouping of text into appropriate categories requires pre-processing techniques and machine learning algorithms. Preprocessing or data cleaning involves removal of html characters, tokenization, stop words removal, stemming, lemmatization and advanced processes such as parts of speech tagging followed by representation in appropriate form for machine learning. This paper experimentally evaluates the impact of stemming and tokenization techniques on text classification on five text datasets.
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