Sentiment Classification of Indian Banks' Customer Complaints

Krishna, Gutha Jaya; Ravi, Vadlamani; Reddy, Bheemidi Vikram; Zaheeruddin, Mohammad; Jaiswal, Harshal; Teja, P. Sai Ravi; Gavval, Rohit

doi:10.1109/tencon.2019.8929703

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…The study's dataset was collected from reference books, journals, similar theses, YouTube, and related articles. Authors in [38] applied Random Forest and Naïve Bayes techniques to address sentiment detection in raw textual data of complaints about an Indian bank, where the data were labeled as "moderate" or "extreme".…”

Section: B Traditional Machine Learning Methods For Sentiment Analysi...mentioning

confidence: 99%

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Haddadi,

Khoeini,

Salmani

et al. 2023

Preprint

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The competitive landscape of a country's banking sector necessitates an in-depth understanding of customer satisfaction levels concerning the services provided. Presently, customers predominantly express their feedback via social media platforms in the form of posts and comments. This study endeavors to create a highly accurate sentiment detection algorithm for the Iranian banking system, utilizing a transformer model. In the initial stages, we collected data by crawling comments from Twitter, which are subsequently labeled and filtered according to the names of Iranian banks, dating from 2019. Following this, an optimized Deep Neural Network (DNN)-based pre-trained ParsBERT model, a monolingual Persian model, is fine-tuned using this data. Finally, our model is evaluated on a test dataset, and the results are validated by comparing them with the original multilingual BERT, Bidirectional Long Short-Term Memory (Bi-LSTM) network, and four other classification methods. To address the Out-Of-Vocabulary (OOV) issue, a character-level embedding is incorporated in conjunction with the word-level embedding. This approach aids in tackling the multitude of variations observed in non-native words, extracting character-level features using a character-level Bi-LSTM. The proposed model highlights the statistical superiority of our method when compared to the other methods evaluated.

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Section: B Traditional Machine Learning Methods For Sentiment Analysi...mentioning

confidence: 99%

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Haddadi,

Khoeini,

Salmani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The experimental results showed that the classifiers achieved encouraging results in directing support requests to related services. Krishna et al (2019) performed sentiment analysis of bank customers using the respective banks' online complaints platforms. They experimented with SVM, NB, LR, Decision Tree (DT), kNN, RF, XGBoost, and Multi-layer Perceptron (MLP) classifiers on data generated of TF-IDF, word2Vec and Linguistic Inquiry and Word Count (LIWC) vectors.…”

Section: Related Workmentioning

confidence: 99%

Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

Bozyiğit¹,

Doğan²,

Kılınç³

2022

Journal of Intelligent Systems: Theory and Applications

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Customer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.

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“…Banking industries have used data mining techniques in various applications, especially on bank failure prediction [1][2][3], possible bank customer churns identification [4], fraudulent transaction detection [5], customer segmentation [8][9][10], predictions on bank telemarketing [11][12][13][14], and sentiment analysis for bank customers [15]. Some of the classification studies in the banking sector have been compared in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…Apart from novel task-specific algorithms proposed by the authors, the most commonly used classification algorithms in the banking sector are decision tree (DT), neural network (NN), support vector machine (SVM), k-nearest neighbor (KNN), Naive Bayes (NB), and logistic regression (LR), as shown in Table 1. Some data mining studies in the banking sector [1,2,6,11,15] have used ensemble learning methods to increase the classification performance. Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages.…”

Section: Related Workmentioning

confidence: 99%

“…Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages. Random forest (RF) [2,6,11,15], AdaBoost (AB) [6], and extreme gradient boosting (XGB) [2,15] have also been used in the banking sector as the most well-known bagging and boosting algorithms, respectively. As shown in Table 1, accuracy (ACC) and area under ROC curve (AUC) are the commonly used performance measures for classification.…”

Section: Related Workmentioning

confidence: 99%

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Data Mining in Banking Sector Using Weighted Decision Jungle Method

Birant¹

2021

Data Mining - Methods, Applications and Systems

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Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction. However, traditional classification algorithms do not take into account the class distribution, which results into undesirable performance on imbalanced banking data. To solve this problem, this paper proposes an approach which improves the decision jungle (DJ) method with a class-based weighting mechanism. The experiments conducted on 17 real-world bank datasets show that the proposed approach outperforms the decision jungle method when handling imbalanced banking data.

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Sentiment Classification of Indian Banks' Customer Complaints

Cited by 10 publications

References 20 publications

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Persian Sentiment Analysis via a Transformer Model concerning Banking Sector

Categorization of Customer Complaints in Food Industry Using Machine Learning Approaches

Data Mining in Banking Sector Using Weighted Decision Jungle Method

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