Optimization of Sentiment Analysis Methods for classifying text comments of bank customers

Lutfullaeva, M.; Medvedeva, Marina A.; Komotskiy, E. I.; Spasov, Kamen

doi:10.1016/j.ifacol.2018.11.353

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…Any sentiment analyses are based on a review of the product's user summarization scheme, such as in Liu et al (2012). The use of a text SA approach and its approval in solving the issue of analyzing texts of reviews left by bank customers is shown by Lutfullaeva et al (2018). They used logistic regression with regularization to classify the reviews.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Hossain

Rahman

Uddin

et al. 2022

JIMA

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Purpose There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches. Design/methodology/approach The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class. Findings The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy. Practical implications The results facilitate halal restaurateurs in identifying customer review behavior. Social implications Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews. Originality/value This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

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Section: Sentiment Analysismentioning

confidence: 99%

“…The use of a text SA approach and its approval in solving the issue of analyzing texts of reviews left by bank customers is shown by Lutfullaeva et al (2018). They used logistic regression with regularization to classify the reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Hossain

Rahman

Uddin

et al. 2022

JIMA

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“…Logistic regression (Lutfullaeva et al, 2018) is among the most popular algorithms for classification problems; it is a transformation of linear regression using the sigmoid function. In general, LR is used to relate one categorical dependent variable to one or more independent variables, and its equation has the following form:…”

Section: Logistic Regressionmentioning

confidence: 99%

Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic

Habbat¹,

Anoun²,

Hassouni³

2022

IJIAS

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Twitter Sentiment Analysis is the task of detecting opinions and sentiments in tweets using different algorithms. In our research work, we conducted a study to analyze and compare different Algorithms of Machine Learning (MLAs) for the classification task, and hence we collected 37 875 Moroccan tweets, during the COVID-19 pandemic, from 01 March 2020 to 28 June 2020. The analysis was done using six classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest classifier) and considering Accuracy, Recall, Precision, and F-Score as evaluation parameters. Then we applied topic modeling over the three classified tweets categories (negative, positive, and neutral) using Latent Dirichlet Allocation (LDA) which is among the most effective approaches to extract discussed topics. As result, the logistic regression classifier gave the best predictions of sentiments with an accuracy of 68.80%.

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“…First is the sentiment analysis as one part of computational linguistics that has a role in studying opinions, emotions, and methods designed to identify and detect emotional reactions or bank consumer attitudes/sentiments expressed in the text. The other one is customer segmentation analysis -a complex process that requires knowledge, skills, experience in big data regarding financial product sales, market understanding and intuition (Lutfullaeva et al, 2018). The purpose of segmentation is not to identify any group of users in a particular market, but rather to find users who have different financial service requirements (Mihova & Pavlov, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

The state of implementing big data in banking business processes: An Indonesian perspective

Ritchi

Andriani

zulkarnaen

et al. 2022

Banks and Bank Systems

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Notwithstanding the perceived global potentiality, how big data enhances decision-making quality prompts an intriguing inquiry, especially in an increasingly competitive banking environment in developing economies. Building on an industry data-driven framework, this study strives to understand the state of implementing big data in the Indonesian banking sector. A deductively organized descriptive method employing in-depth interviews was conducted with subject matter experts representing Indonesian banking-related areas. The result and the following analysis show the modest status of big data implementation across three major banks and two complementary companies, as indicated by many elements of the framework phases that were found during the early adoption stage. This denotes a steady buy-in across banking business processes as particularly reflected in the framework’s four phases – continuing push to meet the variety aspect (intelligence), structured data architecture domination (design), limited choice of performance indicator for big data value (choice), and customer–corporate vision decoupling (implementation). While Indonesian banks have evidently initiated the big data implementation, further improvement remains imperative for the decision-making process. Accordingly, big data should be tightly coupled with a strong data-driven vision that drives decision-making across intra-firm actors. Handling data omnipresence shall be viewed as the embodiment of a data-driven vision.

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Optimization of Sentiment Analysis Methods for classifying text comments of bank customers

Cited by 7 publications

References 3 publications

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic

The state of implementing big data in banking business processes: An Indonesian perspective

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