Due to the large amounts of words usually present in documents, some of their appearances can complicate the classification process and make it less accurate. Accordingly, word representation methods have been employed to handle this issue through the use of a comparative study. In this study, we compare the effectiveness of both word embedding and TF-IDF weighting schema by applying four classifiers to assess the accuracy of the classification. To evaluate the effectiveness of our study, it was tested on the popular 20Newsgroup text document dataset. Following our experimentation, we found that using the TF-IDF method and ANN classifiers on the 20Newsgroup dataset greatly enhanced the text documents' classification compared against the use of word embedding and other classifiers.
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Different Word Representation for Text Classification: A Comparative Study
Semantic Scholar · Computer Science · 2019
Abstract
Due to the large amounts of words usually present in documents, some of their appearances can complicate the classification process and make it less accurate. Accordingly, word representation methods have been employed to handle this issue through the use of a comparative study. In this study, we compare the effectiveness of both word embedding and TF-IDF weighting schema by applying four classifiers to assess the accuracy of the classification. To evaluate the effectiveness of our study, it was tested on the popular 20Newsgroup text document dataset. Following our experimentation, we found that using the TF-IDF method and ANN classifiers on the 20Newsgroup dataset greatly enhanced the text documents' classification compared against the use of word embedding and other classifiers.