A Classification Based Model to Assess Customer Behavior in Banking Sector

Rahman, Abdur; Khan, M. N. A.

doi:10.48084/etasr.1917

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Competitive companies use text-mining tools to better understand client experiences and extract valuable information from social networking platforms, newspapers, and other sources. In [10], k-Nearest Neighbors (k-NN), Decision Trees (DT), and Artificial Neural Network (ANN) models were used to identify client behavior in the banking industry. As a result, a mix of sentiment analysis, text mining, and other methods is critical in ensuring that businesses understand and capitalize on online consumer evaluations.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Kausar

Fageeri

Soosaimanickam

2023

Eng. Technol. Appl. Sci. Res.

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Online retailers and merchants increasingly request feedback from their clients on the products they purchase. This has led to a significant increase in the number of product reviews posted online, as more people are making purchases online. The opinions expressed in these customer reviews have a significant impact on other customers' purchase decisions, as they are influenced by other customers' recommendations or complaints. This study used Amazon, a well-known and widely used e-commerce platform, to examine sentiment categorization using several machine learning techniques while analyzing an Amazon Reviews dataset. At first, the reviews were transformed into vector representations using the Bag-of-Words approach. Word cloud was used to illustrate the text data in terms of the frequency they appear in the review. Subsequently, the machine learning methods decision trees and logistic regression were used. The two models used in this study achieved high levels of accuracy in analyzing the dataset. Specifically, the Decision Tree model outperformed the Logistic Regression one, achieving an impressive accuracy of 99% compared to the 94% of the latter.

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Section: Related Workmentioning

confidence: 99%

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Kausar

Fageeri

Soosaimanickam

2023

Eng. Technol. Appl. Sci. Res.

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“…In recent years, the ML based Text Classification (TC) techniques have been widely employed in various domains [10] such as Sentiment analysis [11,12], E-Commerce portals [13,14], Email classification [15], Human Resource Management [2], Banking and Exchange Stock Markets [16,17], and bioinformatics [18,19]. In this study, ML-based text classification techniques are employed in the Human Resource Management domain.…”

Section: Related Workmentioning

confidence: 99%

Resume Classification System using Natural Language Processing and Machine Learning Techniques

Ali

Mughal

Khan

et al. 2022

murjet

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The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS.

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“…Several companies have employed customer information to track and assess how consumers use their offerings, but more significantly to advertise and suggest new deals or notifications [7]. According to [8][9], companies that manage customer knowledge are more inclined to spot new market possibilities or changes in consumer behavior before competitors. This helps to provide the business, its investors, and its clients with an insightful financial perspective.…”

Section: Introductionmentioning

confidence: 99%

Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective

Khan,

Shao

2024

Eng. Technol. Appl. Sci. Res.

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This study aims to systematically review the literature on the impact of big data and knowledge management on customer interactions and consumption patterns from an applied science perspective. A comprehensive search strategy was implemented in seven scientific publication databases. The inclusion criteria consisted of original research articles published in English, excluding gray literature, book chapters, and conference proceedings. A total of 400 articles were retrieved, and 40 articles met the inclusion criteria after two rounds of screening. The selected articles were analyzed following a mixed-method approach incorporating qualitative and quantitative data analysis techniques. Thematic analysis was deployed to identify recurring themes and patterns in the articles, while descriptive statistics were used to summarize the study characteristics. The data analysis showed that big data and knowledge management significantly affect customer interactions and consumption patterns, with most studies focusing on the retail and banking sectors. The findings of this study have several theoretical and practical implications. From a theoretical point of view, this review contributes to the growing body of literature on the intersection of big data, knowledge management, and consumer behavior. From a practical perspective, the results can inform policymakers and practitioners on leveraging big data and knowledge management in order to improve customer interactions and consumption patterns.

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A Classification Based Model to Assess Customer Behavior in Banking Sector

Cited by 8 publications

References 16 publications

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews

Resume Classification System using Natural Language Processing and Machine Learning Techniques

Impact of Big Data and Knowledge Management on Customer Interactions and Consumption Patterns: Applied Science Research Perspective

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