Visualising Arabic sentiments and association rules in financial text
Issue Date
2017-02-28Subjects
Arabic languagesentiment analysis
text mining
natural language processing
G760 Machine Learning
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Show full item recordAbstract
Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class.Citation
AL-Rubaiee H, Qiu R, Li D (2017) 'Visualising Arabic sentiments and association rules in financial text', International Journal of Advanced Computer Science and Applications, 8 (2)Publisher
SAIType
ArticleLanguage
enISSN
2158-107XEISSN
2156-5570ae974a485f413a2113503eed53cd6c53
10.14569/IJACSA.2017.080201
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