A wavelet technique for the study of economic socio-political situations in a textual analysis framework. Issue 3 (13th August 2018)

• Record Type: Journal Article
• Authors: Abdessalem, Habiba; Benammou, Saloua
• Is Part Of: Journal of economic studies. Volume 45:Issue 3(2018)
• Page Start: 586
• Page End: 597
• Publication Date: 2018-08-13
• Subjects: Textual data -- Correspondence analysis -- K-means classification -- Socio-political situations -- Wavelet thresholding; Economics -- Periodicals; 330.05
• DOI: 10.1108/JES-08-2017-0231
• Languages: English
• ISSNs: 0144-3585

Abstract

Abstract : Purpose: The purpose of this paper is to apply the wavelet thresholding technique in order to analyze economic socio-political situations in Tunisia using textual data sets. This technique is used to remove noise from contingency table. A comparative study is done on correspondence analysis and classification results (using k-means algorithm) before and after denoising. Design/methodology/approach: Textual data set is collected from an electronic newspaper that offers actual economic news about Tunisia. Both the hard and the soft-thresholding techniques are applied based on various Daubechies wavelets with different vanishing moments. Findings: The results obtained have proved the effectiveness of wavelet denoising method in textual data analysis. On one hand, this technique allowed reducing the loss of information generated by correspondence analysis, ensured a better quality of representation of the factorial plan, neglected the interest of lemmatization in textual analysis and improved the results of classification by k-means algorithm. On the other hand, the proximities provided by the factorial visualization validate the economic situation of Tunisia during the studied period showing mainly a stable situation before the revolution and a deteriorated one after the revolution. Originality/value: The results are the first to analyze economic socio-political relations using textual data. The originality of this paper comes also from the joint use of correspondence analysis and wavelet thresholding in textual data analysis.