A Comparative Approach Oftext Mining: Classification, Clustering Andextraction Techniques

Rao, Surya Bhupal

doi:10.26782/jmcms.spl.5/2020.01.00010

Cited by 7 publications

(4 citation statements)

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“…Most words only appear once in the texts that include them. Therefore, the term frequency‐inverse document frequency (TF‐IDF) 36 metric is not representative. To address this issue, some researchers enrich data contexts with external information and resources such as Wikipedia 37 and ontologies 38 .…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, we need a numerical representation of the text to perform calculations. We have chosen to use the BoW 36 representation since it ignores the grammatical structure. The BoW model is utilized to transform the transaction descriptions into a representation better suited for machine learning.…”

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

“…The second step is to use this vocabulary from all the words with their respective word count, to create the vectors for each of the descriptions. Thus the vectors' length is equal to the size of the vocabulary.Other methods, such as Word2Vec or term frequency‐inverse document frequency (TF‐IDF) 36 are more sophisticated but are not required in this case. This is because the description is a short text of the merchant name and activity combined with the transaction label (5–25 letters) and there is no semantics in the description.…”

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

“…Other methods, such as Word2Vec or term frequency‐inverse document frequency (TF‐IDF) 36 are more sophisticated but are not required in this case. This is because the description is a short text of the merchant name and activity combined with the transaction label (5–25 letters) and there is no semantics in the description.…”

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

See 3 more Smart Citations

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

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New business models underpinned by new standards of openness, flexibility, and agility are arising in the banking sector. Therefore, banks have to establish new strategies to keep and to extend their client base, especially with the explosive growth of customers' data and interaction touch-points. Hence, banks and fintechs are in fierce competition to transform customers' data that incorporate their transactions into pertinent and significant knowledge for decision making. In this article, we present a novel system aimed toward solving a long-standing and challenging issue: obtaining classifiers to automatically categorize bank transactions for a Smart Budget Manager. We fit and test our system using real data. The strength of our system lies in the novel combination of incremental machine learning algorithms with a natural language understanding for a fine-grained categorization of bank transactions. Our system serves as a base layer for other advanced banking applications such as segmentation, next best offers and credit scoring. Our system is deployed in a real banking application of a Tunisian bank and has shown bank and customer's satisfaction.

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Section: Related Workmentioning

confidence: 99%

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

Section: Sbm Processing Workflow and Modelsmentioning

confidence: 99%

See 2 more Smart Citations

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

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RETRACTED CHAPTER: An Experimental Investigation of PCA-Based Intrusion Detection Approach Utilizing Machine Learning Algorithms

Kumar

Seshanna

Basha³

et al. 2021

Mobile Computing and Sustainable Informatics

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A novel sampling-based visual topic models with computational intelligence for big social health data clustering

et al. 2022

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Twitter is a popular social network for people to share views or opinions on various topics. Many people search for health topics through Twitter; thus, obtaining a vast amount of social health data from Twitter is possible. Topic models are widely used for social health-care data clustering. These models require prior knowledge about the clustering tendency. Determining the number of clusters of given social health data is known as the health cluster tendency. Visual techniques, including visual assessment of the cluster tendency, cosine-based, and multiviewpoint-based cosine similarity features VAT (MVCS-VAT), are used to identify social health cluster tendencies. The recent MVCS-VAT technique is superior to others; however, it is the most expensive technique for big social health data cluster assessment. Thus, this paper aims to enhance the work of the MVCS-VAT using a sampling technique to address the big social health data assessment problem. Experimental is conducted on different health datasets for demonstrating an efficiency of proposed work. Accuracy of social health data clustering is improved at a rate of 5 to 10% in the proposed S-MVCS-VAT when compared to MVCS-VAT. From obtained results, it also proved that the proposed S-MVCS-VAT is a faster and memory efficient for discovering social health data clusters.

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A Comparative Approach Oftext Mining: Classification, Clustering Andextraction Techniques

Cited by 7 publications

References 0 publications

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

RETRACTED CHAPTER: An Experimental Investigation of PCA-Based Intrusion Detection Approach Utilizing Machine Learning Algorithms

A novel sampling-based visual topic models with computational intelligence for big social health data clustering

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