Giving Structure to Unstructured Text Data by Employing Classification
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Ngetich Ngor Gogo, Matthias Daniel, Alabo Gift.|
How to Cite?
Ngetich Ngor Gogo, Matthias Daniel, Alabo Gift, "Giving Structure to Unstructured Text Data by Employing Classification," International Journal of Computer Trends and Technology, vol. 69, no. 2, pp. 22-28, 2021. Crossref, 10.14445/22312803/IJCTT-V69I2P104
As relevant as the need to have information readily available and well manage; quite a volume of information are inaccessible and locked up in a huge volume of text documents (unstructured data) that could be applied in the economy by the government, individuals, and corporate organization to ameliorate on the state of life and develop better working system; this cannot be overemphasized, therefore the need to extract this information and give a structure that will expedite adequate management, storage, and access when required because of their importance. The aim of this research is to implement a Classification Algorithm as a technique for giving Structure to Unstructured Data (Text document). The Multinomial Naïve Bayes classifier Algorithm was deployed for the purpose of classifying these unstructured data to give structure to it. There are two major phases involved in this: first is the pre-processing phase (Tokenization, Stemming, and Stop Word Removal), and second the Classification phase. The system built performed better, as shown from the result, that it can be used to classify text documents for proper and easy management, storage, and accessibility.
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Structure, Unstructured data, Classification, Multinomial Naïve Bayes classifier, Algorithm, pre-processing