Authors:
Hasan Hameed Hasan Ahmed Abdulla
and
Wasan Shakir Awad
Affiliation:
College of Information Technology, Ahlia University, Exhibition Road, Manama, Bahrain
Keyword(s):
Graph Mining, News Articles, Text, Classification, Algorithm, Language Processing, Weighting Scheme.
Abstract:
Several techniques can be used in the natural language processing systems to understand text documents, such as, text classification. Text Classification is considered a classical problem with several purposes, varying from automated text classification to sentiment analysis. A graph mining technique for the text classification of English news articles is considered in this research. The proposed model was examined where every text is characterized by a graph that codes relations among the various words. A word's significance to a text is presented by the graph-theoretical degree of a graph's vertices. The proposed weighting scheme can significantly obtain the links between the words that co-appear in a text, producing feature vectors that can enhance the English news articles classification. Experiments have been conducted by implementing the proposed classification algorithms in well-known text datasets. The findings suggest that the proposed text classification using graph mining
technique as accurate as other techniques using appropriate parameters.
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