Abstract
Keyword extraction of complex hierarchical categories becomes a challenge in text mining since commonly used classification for flat categories results in low accuracy. This paper presents a method to improve keyword extraction from hierarchical categories considering terms occurred in category from a hierarchy as additional factors in term-weighting. The method is an enhancement of a basic TF-IDF calculation; thus, it can comfortably be used for keyword extraction and classification. By taking term frequency and inverse document frequency of categories hierarchically related to a focused category, we can determine how important terms are in their family categories. In this work, hierarchy relations used in calculation are sub-categories, supercategories and sibling-categories. From experiment results, we found that the proposed method gained higher accuracy for about 40% from a baseline in a classification task.
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Author would like to thank National Reform Council for providing comment data from Thai Reform Website.
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Chiraratanasopha, B., Theeramunkong, T., Boonbrahm, S. (2019). Improved Term Weighting Factors for Keyword Extraction in Hierarchical Category Structure and Thai Text Classification. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_6
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DOI: https://doi.org/10.1007/978-3-319-94703-7_6
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