A wavelet technique for the study of economic socio-political situations in a textual analysis framework

Abdessalem, Habiba; Benammou, Saloua

doi:10.1108/jes-08-2017-0231

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, wavelet theory as a mathematical tool is recently introduced in the field of finance and proved its performance compared to classical tools. In the field of textual analysis, in our knowledge, the present work is a first application of such a method, among [8] and [6]. In [8], the subject was about economic applications similar to the present work, and in [6] the application has dealt with information processing.…”

Section: Introductionmentioning

confidence: 89%

“…In the field of textual analysis, in our knowledge, the present work is a first application of such a method, among [8] and [6]. In [8], the subject was about economic applications similar to the present work, and in [6] the application has dealt with information processing. In the present work, one aim is to continue to prove the efficiency and the performance of such a method by applying it to different type of media texts.…”

Section: Introductionmentioning

confidence: 89%

“…In the present work, the method consists in applying wavelet theory essentially at the step of denoising the contingency table (seen as an image) [8]. Indeed, classical textual analysis suffers from one main disadvantage due to noised results and especially contingency matrices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

2021

Self Cite

View full text Add to dashboard Cite

In the present paper, a wavelet method is proposed to study the impact of electronic media on economic situation. More precisely, wavelet techniques are applied versus classical methods to analyze economic indices in the market. The technique consists firstly of filtering the data from unprecise circumstances (noise) to construct next a wavelet denoised contingency table. Next, a thresholding procedure is applied to such a table to extract the essential information porters. The resulting table subject finally to correspondence analysis before and after thresholding. As a case of study, the KSA 2030-vision is considered in the empirical part based on electronic and social media. Effects of the electronic media texts about the trading 2030 vision on the Saudi and global economy has been studied. Recall that the Saudi market is the most important representative market in the GCC continent. It has both regional and worldwide influence on economies and besides, it is characterized by many political, economic and financial movements such as the worldwide economic NEOM project. The findings provided in the present paper may be applied to predict the future situation of markets in GCC region and may constitute therefore a guide for investors to decide about investing or not in these markets.

show abstract

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 89%

See 1 more Smart Citation

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Apart from those aforementioned denoising methods, DWT, providing good localization in both spatial and frequency domains has been an efficiently useful tool for image feature extraction as a preprocess for noise removal (Cai, 2019;Abdessalem and Benammou, 2018), while most of image details still remain, and none of training image set is needed for feature learning. DWT decomposes an image into different subband images; then, noise reduction is carried out in those subbands; finally, inverse DWT is employed to reconstruct a denoised image.…”

Section: Introductionmentioning

confidence: 99%

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

Pidchayathanakorn

Supratid

2021

DTA

View full text Add to dashboard Cite

PurposeA major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).Design/methodology/approachHere, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.FindingsImplicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.Research limitations/implicationsA future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.Practical implicationsThis paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.Originality/valueIn most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

show abstract

Application of Textual Analysis in Economic Policy Uncertainty

Xu¹,

Hu²,

Tian³

2019

Proceedings of the Fourth International Conference on Economic and Business Management (FEBM 2019)

View full text Add to dashboard Cite

After the financial crisis, in order to alleviate the attendant rising unemployment, inflation, balance of payments imbalances, and the economic downturn, the government has to consider more involvement in addressing market failures by designating economic policies. This paper mainly introduces the economic policy uncertainty and some economic problems resulted from it. This paper explores the feasibility of textual analysis in solving EPU problems by method of case study and points out the limitations in the application process. Through literature review, the limitations of text analysis in China have been found-high context culture and a large number of unrelated economic policy reports have become obstacles. Based on the methodology of EPU and text analysis, the cases of the United States and Tunisia were analyzed to confirm the role of text analysis in measuring EPU issues. Finally, the conclusion is drawn that China should refer to the above two cases and combine the national conditions to solve the EPU problem.

show abstract

A wavelet technique for the study of economic socio-political situations in a textual analysis framework

Cited by 3 publications

References 21 publications

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

Application of Textual Analysis in Economic Policy Uncertainty

Contact Info

Product

Resources

About