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PUBLISHED DATE: - 21-10-2024
https://doi.org/10.37547/tajet/Volume06Issue10-11
PAGE NO.: - 100-111
A COMPREHENSIVE STUDY OF MACHINE
LEARNING APPROACHES FOR CUSTOMER
SENTIMENT ANALYSIS IN BANKING SECTOR
Salma Akter
Department of Public Administration, Gannon University, Erie, PA, USA
Fuad Mahmud
Department of Information Assurance and Cybersecurity, Gannon
University, USA
Tauhedur Rahman
Dahlkemper School of Business, Gannon University, USA
Md Jamil Ahmmed
Department of Information Technology Project Management, Business
Analytics, St. Francis College, USA
Md Kafil Uddin
Dahlkemper School of Business, Gannon University, USA
Md Imdadul Alam
Master of Science in Financial Analysis, Fox School of Business, Temple
University, USA
Biswanath Bhattacharjee
Department of Management Science and Quantitative Methods, Gannon
University, USA
Sharmin Akter
Department of Information Technology Project Management, St. Francis
College, USA
Md Shakhaowat Hossain
Department of Management Science and Quantitative Methods, Gannon
University, USA
RESEARCH ARTICLE
Open Access
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Afrin Hoque Jui
Department of Management Science and Quantitative Methods, Gannon
University, USA
INTRODUCTION
Sentiment analysis has gained significant traction
in the realm of Natural Language Processing (NLP)
as businesses seek to derive actionable insights
from customer feedback. In the banking sector,
understanding customer sentiment is critical for
enhancing service delivery, maintaining customer
loyalty, and staying competitive in an increasingly
digital marketplace. The explosion of digital
interactions
—
ranging
from
social
media
commentary to formal feedback mechanisms
—
has
created a vast repository of customer opinions
that, when analyzed, can yield deep insights into
consumer behavior and preferences.
In recent years, the banking industry has
witnessed a transformation characterized by the
adoption of various technological advancements,
which have changed the landscape of customer
interactions (Sinha & Kaur, 2020). As financial
institutions strive to provide personalized services
and real-time customer support, sentiment
analysis plays a pivotal role in understanding
customer needs and improving overall satisfaction
(Akhtar et al., 2022). This study aims to explore the
intricacies of sentiment analysis in the banking
sector by leveraging machine learning techniques
to classify sentiments from customer feedback,
thereby providing a comprehensive understanding
of customer experiences across various banking
services.
The research is anchored in the premise that
effectively analyzing sentiment can facilitate not
only improved customer service but also informed
decision-making regarding product offerings and
service enhancements (Bahl et al., 2021). With this
objective, our study employs various machine
learning models
—
including Logistic Regression,
Naive Bayes, Support Vector Machine (SVM),
Random Forest, Long Short-Term Memory (LSTM)
networks,
and
Bidirectional
Encoder
Representations from Transformers (BERT)
—
to
classify
sentiments
and
evaluate
their
performance based on multiple metrics.
Abstract
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LITERATURE REVIEW
Overview of Sentiment Analysis
Sentiment analysis is a subfield of NLP that focuses
on identifying and categorizing opinions expressed
in textual data. It aims to classify sentiments as
positive, negative, or neutral and has become
increasingly important due to the proliferation of
online reviews and feedback across various
industries (Pang & Lee, 2008). The significance of
sentiment analysis lies in its ability to provide
businesses
with
insights
into
customer
perceptions, allowing them to respond proactively
to emerging trends and sentiments (Liu, 2012).
Machine Learning Techniques for Sentiment
Analysis
The application of machine learning techniques in
sentiment analysis has proven effective, with
various algorithms demonstrating differing
strengths and limitations. Logistic Regression and
Naive Bayes are commonly utilized as baseline
models due to their simplicity and efficiency (Yin
et al., 2016). Logistic Regression offers
interpretability but may struggle with complex
sentiment patterns, while Naive Bayes performs
well with high-dimensional data, albeit with
limitations in understanding word context (Rish,
2001).
Support Vector Machine (SVM) has emerged as a
robust
classifier
for
sentiment
analysis,
particularly due to its ability to handle high-
dimensional spaces and its effectiveness in dealing
with noisy data (Joachims, 1999). Random Forest,
an ensemble learning method, provides improved
accuracy and robustness against overfitting by
aggregating the predictions of multiple decision
trees (Breiman, 2001).
Recent advances in deep learning have introduced
more sophisticated models such as Recurrent
Neural Networks (RNN) with Long Short-Term
Memory (LSTM) units. LSTMs excel at capturing
sequential dependencies in data, making them
well-suited for sentiment analysis in lengthy and
complex reviews (Hochreiter & Schmidhuber,
1997). On the cutting edge of sentiment analysis
are transformer-based models like BERT, which
have set new benchmarks by considering the
context of each word from both directions, thereby
achieving superior performance in sentiment
classification tasks (Devlin et al., 2018).
Challenges in Sentiment Analysis
Despite the advancements in machine learning
techniques, sentiment analysis continues to face
several challenges. One significant issue is the
presence of mixed sentiments within single
comments, where customers express both positive
and negative opinions, complicating the
classification process (Cambria et al., 2017).
Furthermore, unstructured feedback often
includes informal language, slang, and emoticons,
which can hinder accurate sentiment classification
(Pang & Lee, 2008).
Data imbalance is another challenge encountered
in sentiment analysis, especially in domains like
banking, where certain sentiments may be
underrepresented in the dataset (He & Garcia,
2009). This imbalance can bias machine learning
models, making them less effective at accurately
predicting minority classes. Techniques such as
Synthetic Minority Over-sampling Technique
(SMOTE) and under-sampling are often employed
to address these imbalances and enhance model
performance (Chawla et al., 2002).
Importance of Feature Engineering
Feature engineering is a crucial aspect of
sentiment analysis that directly impacts the
performance of machine learning models.
Techniques such as Term Frequency-Inverse
Document Frequency (TF-IDF), n-grams analysis,
and Part-of-Speech (POS) tagging are commonly
used to extract meaningful features from textual
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data (Manning et al., 2008). These techniques help
identify key phrases, sentiment-bearing words,
and contextual relationships that are critical for
effective sentiment classification.
METHODOLOGY
1. Data Collection and Preprocessing
In conducting our sentiment analysis, the first
critical step was collecting an extensive and
representative dataset of customer feedback from
various banking services. To ensure our analysis
covered a broad spectrum of customer
experiences, we pulled feedback from multiple
sources. These included customer satisfaction
surveys, online banking reviews, social media
platforms like Twitter and Facebook, as well as
direct customer emails and feedback submitted
through the bank's official mobile app.
1.1 Data Collection Process
We approached the data collection phase
methodically to ensure the richness and diversity
of the feedback. The data was sourced over a two-
year period, resulting in a comprehensive
collection of around 100,000 customer feedback
entries. This dataset spanned various aspects of
banking services, including online banking, in-
branch experiences, credit and loan services,
mobile app functionality, and customer support
interactions. Our aim was to cover both structured
feedback (like survey responses) and unstructured
feedback (such as free-form comments on social
media and emails).
The feedback was gathered from customers across
different demographics and geographical regions,
providing us with insights into how customer
experiences and sentiments varied by location,
age, and service type. Additionally, we ensured the
inclusion of a range of banking services, which
allowed us to target specific service areas that
might need improvement.
1.2 Data Challenges
Collecting and preparing data for sentiment
analysis posed several challenges, particularly
with the unstructured nature of the customer
feedback. A significant portion of the comments
contained informal language, abbreviations,
emoticons, and even mixed languages, particularly
when dealing with social media data. Furthermore,
many reviews were either too short, offering little
context (e.g., "bad service" or "great app"), or too
complex, with customers expressing multiple
sentiments within a single review (e.g., "The
mobile app is great, but customer service is slow").
To address these issues, we implemented a multi-
step data cleaning and preprocessing pipeline that
allowed us to structure the unstructured data in a
meaningful way, ensuring that we could maximize
the quality of the analysis.
1.3 Preprocessing Steps
We recognized that quality preprocessing was
essential to extracting actionable insights from the
raw feedback data. Our preprocessing pipeline
consisted of several stages:
•
Text Cleaning: The feedback contained
various forms of noise, such as URLs, special
characters, HTML tags, numbers, and emojis. We
removed these elements to focus on the core
textual content. Additionally, feedback with
minimal word count (e.g., single-word reviews)
was filtered out, as they provided insufficient
sentiment context.
•
Tokenization: We broke down the sentences
into individual words or tokens to analyze them
more efficiently. This step was crucial in
separating the components of complex sentences
where customers expressed different sentiments
in a single review. For instance, if a customer said,
"The loan process was difficult, but the customer
support was helpful," tokenization allowed us to
treat "loan process was difficult" and "customer
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support was helpful" as separate sentiments.
•
Lemmatization and Stemming: After
tokenization, we applied lemmatization to reduce
words to their base or dictionary form. This
allowed us to avoid treating variations of the same
word as separate entities. For example, the words
"banking," "bank," and "banks" were all reduced to
the base form "bank." We found that lemmatization
improved the accuracy of sentiment classification
as compared to using stemming, which often led to
distorted word forms. However, stemming was
still employed for some models, depending on
their requirements, and comparative studies were
done to evaluate the performance differences.
•
Stop Word Removal: We identified and
removed common stop words such as "and," "the,"
"is," and "of," which did not contribute to the
sentiment. However, we retained certain domain-
specific stop words relevant to banking, such as
“loan,” “branch,” and “transaction,” to ensure that
key features of customer experiences were
captured.
•
Handling Negations: One of the challenges
we encountered was properly processing
negations. A simple feedback
like “not good” could
easily be misclassified as positive without proper
handling of negation. To address this, we created a
rule-based system that concatenated negation
terms with the words that followed, thus
transforming phrases like "not happy" into
"not_happy," ensuring that the model could
accurately capture the negative sentiment.
1.4 Handling Mixed and Complex Sentiments
A significant portion of the feedback we
encountered contained mixed sentiments, where a
single customer comment included both positive
and negative aspects. For example, a customer
might say, "The loan process was complicated, but
the bank staff were very helpful." This presented a
challenge since traditional sentiment analysis
models often classify such sentences as neutral,
missing the opportunity to extract both
sentiments.
To address this, we employed sentence
segmentation techniques, splitting each feedback
entry into distinct sentences or clauses. By doing
this, we ensured that each sentiment was treated
independently, allowing us to capture the nuance
of customer feedback more effectively. Sentences
were categorized based on their service context,
such as loan services, customer support, or online
banking, which helped us pinpoint specific areas
needing improvement.
1.5 Dealing with Data Imbalance
As is common in sentiment analysis tasks, we
encountered an imbalance in the distribution of
sentiments across different categories. For
instance,
online
banking
feedback
was
overwhelmingly positive, while feedback related
to loan services tended to skew more negative.
This imbalance posed a challenge, particularly for
our machine learning models, as they might
become biased toward predicting the majority
sentiment.
To mitigate this, we experimented with various
techniques, including Synthetic Minority Over-
sampling Technique (SMOTE) to artificially
generate samples of the underrepresented classes,
such as negative feedback on online banking or
positive feedback on loan services. This allowed
our models to train more effectively across all
sentiment categories and prevented overfitting
toward majority sentiment classes. We also used
under sampling for certain service areas where an
overwhelming amount of positive feedback risked
drowning out the insights from the negative
feedback.
1.6 Feature Engineering and Extraction
To enhance the performance of our machine
learning models, we engaged in several feature
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engineering tasks that allowed us to extract more
meaningful insights from the customer feedback
data:
•
N-grams Analysis: We incorporated n-grams
(bigrams and trigrams) to capture phrases that
frequently appeared in the feedback. This enabled
us to identify recurring themes or issues, such as
"customer service delay" or "quick mobile
transfer." The use of n-grams helped the models
understand not just individual word sentiment but
also contextual sentiment from phrases and word
pairs.
•
TF-IDF (Term Frequency-Inverse Document
Frequency): We employed the TF-IDF technique to
weigh the importance of words in the feedback.
This helped the model distinguish between
commonly used words and words that carried
unique sentiment significance. For example, words
like "problem" or "excellent" were given higher
importance than words like "bank" or "account,"
which were present in almost every review.
•
Part-of-Speech (POS) Tagging: To improve
our sentiment classification, we leveraged POS
tagging to identify adjectives, verbs, and adverbs
that carried strong sentiment. Words like "quick"
(adjective) or "solved" (verb) were crucial in
determining the tone of feedback, especially when
combined with customer experiences related to
service speed and problem resolution.
1.7 Final Preprocessed Dataset
By the end of our preprocessing pipeline, we had a
clean, tokenized, and well-structured dataset that
was ready for sentiment classification. Each
feedback entry was categorized into service areas
(e.g., loan services, online banking, customer
support), ensuring that the sentiment analysis
could provide granular insights into specific
banking functions.
The final dataset consisted of the following:
•
Total Feedback Entries: Approximately
100,000
•
Positive Feedback: 58,000 entries (58%)
•
Negative Feedback: 30,000 entries (30%)
•
Neutral Feedback: 12,000 entries (12%)
•
Service-Specific Categorization: Online
banking (30%), in-branch services (20%), loan
services (15%), mobile app feedback (25%), and
customer support (10%).
Our preprocessed data was now ready for the next
phase, where we implemented various machine
learning and Natural Language Processing (NLP)
models to classify sentiment and generate
actionable insights for improving banking services.
RESULT
2.1 Logistic Regression (Baseline Model)
Logistic Regression (LR) is a widely used
classification algorithm that applies a linear model
to estimate the probability of a class (positive,
negative, neutral) based on input features. As a
baseline model, LR was chosen due to its simplicity
and interpretability.
•
Feature
Extraction:
TF-IDF
(Term
Frequency-Inverse Document Frequency) vectors
were used to convert the textual feedback into
numerical features.
•
Strengths: Fast, easy to interpret, handles
overfitting with regularization (L1/L2 penalties).
•
Limitations: Logistic Regression assumes
linear separability of the data, which may not hold
true for complex language patterns in customer
feedback.
2.2 Naive Bayes
Naive Bayes (NB) is another classical ML algorithm
that works particularly well for text classification
tasks, as it assumes that features are conditionally
independent given the class label. We used
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Multinomial Naive Bayes (MNB) due to its
popularity in text-based sentiment analysis.
•
Feature Extraction: TF-IDF vectors were
also used here to represent the customer feedback
data.
•
Strengths: Works well
with high-
dimensional data, especially in cases where the
independence assumption roughly holds. Fast and
efficient.
•
Limitations: Naive Bayes struggles with
complex relationships between words, such as
word order or context, leading to potential
misclassification of sentiment.
2.3 Support Vector Machine (SVM)
SVM is a powerful classification algorithm that
attempts to find the hyperplane that best separates
different classes in the feature space. In sentiment
analysis, SVM is well-regarded for handling high-
dimensional data and dealing with noise in the
dataset.
•
Feature Extraction: We used TF-IDF vectors
for input features.
•
Strengths: SVM is effective in high-
dimensional spaces and is robust to overfitting,
especially in text classification tasks.
•
Limitations: SVM can be computationally
expensive, especially for large datasets. Choosing
the right kernel and regularization parameter can
be challenging.
2.4 Random Forest
Random Forest (RF) is an ensemble learning
algorithm that builds multiple decision trees and
combines their outputs to make a final prediction.
It is popular for its ability to handle non-linear data
and complex decision boundaries.
•
Feature Extraction: TF-IDF vectors were
used to feed the feedback into the Random Forest
model.
•
Strengths: Random Forest is less prone to
overfitting compared to individual decision trees
and can capture complex patterns in the data.
•
Limitations: While Random Forest can
handle complex data, it tends to require a large
number of computational resources and may
struggle with high-dimensional, sparse data typical
in text analysis.
2.5 Recurrent Neural Networks (RNN) with
LSTM
Recurrent Neural Networks (RNNs) with Long
Short-Term Memory (LSTM) units are designed to
capture temporal dependencies and context in
sequential data, making them well-suited for text-
based tasks like sentiment analysis. LSTM
networks can remember long-term dependencies
between words, overcoming limitations of
traditional ML algorithms in NLP.
•
Feature Extraction: Unlike traditional ML
algorithms, LSTM models do not require manual
feature extraction. Instead, we used word
embeddings (Word2Vec and GloVe) to transform
the text into dense vector representations.
•
Strengths: LSTM networks capture context
and word order, making them excellent for
understanding complex sentiments in long
customer reviews.
•
Limitations:
LSTM
models
are
computationally intensive and require more time
for training. Overfitting can be a concern if the
model is not regularized.
2.6
BERT
(Bidirectional
Encoder
Representations from Transformers)
BERT is a transformer-based pre-trained language
model that has achieved state-of-the-art
performance on many NLP tasks, including
sentiment analysis. BERT considers the context of
each word from both directions (left-to-right and
right-to-left) in a sentence, which allows it to
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understand nuanced meaning and relationships
between words.
•
Feature Extraction: BERT uses its pre-
trained embedding layers to encode textual
feedback into contextualized vectors. We fine-
tuned BERT on our specific dataset for sentiment
classification.
•
Strengths: BERT excels at understanding
complex language patterns, including context,
syntax, and sentiment polarity. It has
demonstrated superior performance compared to
traditional models in many NLP applications.
•
Limitations: BERT is computationally
expensive and requires large memory resources.
Fine-tuning BERT can be time-consuming,
especially with large datasets.
COMPARATIVE STUDY
To evaluate the performance of each machine
learning model, we conducted a thorough
comparative study using the following metrics:
•
Accuracy: The ratio of correctly predicted
instances over the total instances.
•
Precision: The ratio of true positives to the
sum of true positives and false positives. It
measures how relevant the positive predictions
are.
•
Recall (Sensitivity): The ratio of true
positives to the sum of true positives and false
negatives. It measures how well the model
captures the actual positives.
•
F1 Score: The harmonic mean of precision
and recall, providing a balance between the two.
•
AUC-ROC Curve: Measures the ability of the
model to distinguish between classes (positive vs.
negative).
•
Training Time: The amount of time required
to train the model, important for scalability and
real-time applications.
3.1 Results Summary
Algorithm
Accuracy Precision Recall F1 Score AUC-ROC Training Time
Logistic Regression
0.80
0.78
0.77
0.77
0.82
Fast
Naive Bayes
0.79
0.76
0.75
0.75
0.80
Very Fast
SVM
0.82
0.80
0.78
0.79
0.84
Moderate
Random Forest
0.83
0.81
0.80
0.80
0.85
Moderate
LSTM
0.85
0.83
0.82
0.83
0.87
High
BERT
0.88
0.87
0.86
0.86
0.90
Very High
0.8
0.79
0.82
0.83
0.85
0.88
0.78
0.76
0.8
0.81
0.83
0.87
0.77
0.75
0.78
0.8
0.82
0.86
0.77
0.75
0.79
0.8
0.83
0.86
0.82
0.8
0.84
0.85
0.87
0.9
L O G I S T I C
R E G R E S S I O N
N A I V E B A Y E S
S V M
R A N D O M
F O R E S T
L S T M
B E R T
E V A L U A T I O N O F M A C H I N E L E A R N I N G A N D N L P
A L G O R I T H M
Accuracy
Precision
Recall
F1 Score
AUC-ROC
Training Time
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3.2 Analysis of Results
1.
Logistic Regression and Naive Bayes: These
baseline models provided decent performance but
were outperformed by more sophisticated models.
While both models are easy to interpret and
computationally efficient, they struggled with
complex language and failed to capture context,
especially in reviews containing mixed or nuanced
sentiments. The accuracy for both hovered around
80%, but their F1 scores indicate they are less
effective in handling imbalanced classes.
2.
SVM: SVM outperformed the baseline
models with an accuracy of 82%. It demonstrated
stronger performance due to its ability to find a
better decision boundary between classes,
especially when sentiment classes (positive,
negative, neutral) were not linearly separable.
However, the trade-off was the longer training
time, especially when tuning the kernel.
3.
Random Forest: Random Forest achieved
better accuracy (83%) and F1 score than Logistic
Regression and Naive Bayes. Its ability to capture
non-linear patterns helped it perform well,
especially on mixed sentiment reviews. However,
the model was slower and required more memory,
making it less feasible for real-time feedback
analysis.
4.
LSTM: The LSTM model provided significant
improvements, especially in its ability to capture
the sequence of words and context within the
customer reviews. With an accuracy of 85% and
high recall and precision, LSTM handled longer,
complex reviews effectively. However, the model
required substantial computational resources and
took a long time to train.
5.
BERT: BERT emerged as the best-
performing model, with an accuracy of 88% and
the highest F1 score of 0.86. Its ability to
understand the context of words in both directions
enabled it to excel in capturing nuanced
sentiments. The AUC-ROC of 0.90 indicated that
BERT was highly effective in distinguishing
between sentiment classes. However, the
downside of BERT was its high computational cost
and long training time, making it less suitable for
quick, real-time analysis unless sufficient
resources are available.
CONCLUSION AND DISCUSSION
In this study, we conducted a comprehensive
sentiment analysis of customer feedback in the
banking sector, employing a diverse range of
machine learning and natural language processing
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(NLP) models. The findings underscore the
importance of understanding customer sentiments
to improve banking services and enhance
customer experiences. Our extensive dataset,
comprising approximately 100,000 entries
collected from various sources, provided a solid
foundation for evaluating different sentiment
classification algorithms.
The comparative analysis revealed that advanced
models,
particularly
BERT
and
LSTM,
outperformed traditional approaches like Logistic
Regression and Naive Bayes in capturing complex
sentiments expressed in customer feedback.
BERT's ability to analyze context by considering
words bidirectionally allowed it to excel in
identifying nuances in customer sentiments,
leading to an impressive accuracy of 88% and an
F1 score of 0.86. This is significant, especially in a
domain where customer sentiment can be
multifaceted and deeply intertwined with their
experiences.
On the other hand, while Logistic Regression and
Naive Bayes served as useful baseline models, their
limitations became evident, particularly in
handling nuanced and mixed sentiments. These
models achieved reasonable performance but
struggled with the complexity inherent in
customer reviews, as seen in their lower F1 scores
and challenges in detecting sentiment imbalances.
The study also highlights the challenges
encountered during data collection and
preprocessing, particularly with unstructured
feedback, informal language, and mixed
sentiments. Our multi-step preprocessing pipeline
effectively addressed these challenges, ensuring a
high-quality dataset for model training. The
application of techniques such as n-grams analysis,
TF-IDF weighting, and POS tagging enriched our
feature extraction process, further enhancing
model performance.
The implications of our findings are significant for
banking institutions. By adopting advanced
sentiment analysis techniques, banks can gain
deeper insights into customer feedback, identify
service areas that require improvement, and
develop targeted strategies to enhance customer
satisfaction. For instance, understanding the
reasons behind negative sentiments related to loan
services can guide banks in streamlining their
processes and training their staff, ultimately
leading to improved customer experiences.
However, it is essential to acknowledge the
computational demands of models like BERT and
LSTM, which may pose challenges for real-time
sentiment analysis in environments with limited
resources. Future research could explore
optimization strategies to balance accuracy with
computational efficiency, ensuring that insights
derived from sentiment analysis can be leveraged
in a timely manner.
In conclusion, our study underscores the
transformative potential of sentiment analysis in
the banking sector. By utilizing advanced machine
learning models, banks can not only improve
service quality but also foster stronger
relationships with their customers. The
continuous evolution of NLP technologies offers
exciting prospects for further research, which can
expand the boundaries of customer sentiment
understanding and its applications in various
domains beyond banking.
Acknowledgement: All the author contributed
Equally
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