Visualising Arabic sentiments and association rules in financial text

Hamed Saad AL-Rubaiee
,
Renxi Qiu
,
Dayou Li

Research Output:

Contribution to journal

Article

Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output:

Contribution to journal

Article

Peer-review

Original language

English

Journal (Volume, Issue Number)

International Journal of Advanced Computer Science and Applications (Volume 8, Issue 2)

Publication milestones

Published - 28/02/2017

Publication status

Published - 28/02/2017

ISSN

2158-107X

Publication IDs

handle.net: 10547/623852

Access to documents

10.14569/IJACSA.2017.080201

Paper_1-Visualising_Arabic_Sentiments_and_Association_Rules.pdf

Accepted author manuscript, 799.72 KB

License:CC BY-NC-ND

https://thesai.org/Publications/ViewPaper%3FVolume%3D8%26Issue%3D2%26Code%3Dijacsa%26SerialNo%3D1

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