Deep learning-driven sentiment analysis for customer satisfaction optimization in airline reviews

The massive social media data bring timely, multi-dimensional and rich information. Recently, many researchers have worked on event summarization with crowdsourced social media data. While existing works mostly focus on text-based summary, they only summarize representative microblogs. Public sentiment for the event is also valuable; however, this is not explored in microblogging event summary. In this paper, we propose SentiStory, which is a multi-grained sentiment analysis and event summarization system that summarizes event from two levels: coarse-grained and fine-grained sentiment analysis. In coarse-grained analysis, it discovers microblogs which are important in sentiment, while in fine-grained analysis, it detects significant change of sentiment in the event and identifies which microblog causes the change. Specifically, the proposed system comprises two modules: (1) the microblog preprocessing module firstly reduces redundant information and extracts useful information from the microblog database, and then, it separates different aspects of the event and clusters the same aspect together in a clue. (2) The multi-grained sentiment analysis model analyzes microblogs from two levels: coarse-grained and fine-grained. We perform detailed experimental study on real dataset collected from Sina Weibo, and the results demonstrate the effectiveness of our approach.

Purpose: The purpose of this paper is to propose a framework of airlines customer’s evaluation of green service quality toward customer’s satisfaction provided by Malaysia Airline Companies.
 
 Design/Methodology/Approach: This paper reviews the conceptual and empirical literature to develop a model of the various aspects of the service quality – customer satisfaction relationship encompassed by the concept of ‘airline customer satisfaction’ and to assess the advantages and disadvantages of alternative methods of measurement. Six conceptual dimensions are identified: (1) Airline tangibles (2) Terminal tangibles (3) Personnel (4) Empathy (5) Image (6) Green Airport (7) Customer Satisfaction
 Findings: Integration of these dimensions into a conceptual framework that describes the relationships among the components and how they collectively result in green service quality and customer satisfaction. We also elucidate future research directions for management scholars
 
 Research Limitation/Implication: The research examines the relationship between green service quality dimension and customer satisfaction in airline industry. This study focuses on the evaluation of all airline service user regarding airline service quality. The main limitation of this study is that it focusses to customer satisfaction on all Airlines Companies: thus, the result cannot be generalized.
 
 Practical Implications: The framework proposed can be applied to future research on green service quality and customer satisfaction. This study will lead to passenger’s satisfaction and encourage the development of long-term relationships with their customers.

Public service is an activity in order to meet the community's needs for goods and services and administrative services provided by public service providers. Service quality has a relationship with community satisfaction, that there is a close relationship between service quality and community satisfaction as well as agency profits where higher service quality results in higher satisfaction. Current developments in technology make it easier for government to provide services for people and carry out transactions. The objectives of this research are: (i) To test and analyze service quality which has a significant positive effect on customer satisfaction. (ii) To test and analyze the use of information systems which has a significant positive effect on customer satisfaction. (iii) To test and analyze the non-cash payment system which has a significant positive effect on customer satisfaction. (iv) To test and analyze service quality which has a significant positive effect on customer loyalty. (v) To test and analyze the use of information technology which has a significant positive effect on customer loyalty. (vi) To test and analyze non-cash payments having a significant positive effect on customer loyalty. (vii) To test and analyze customer satisfaction which has a significant positive effect on customer loyalty. (viii) To test and analyze service quality which has a significant positive effect on customer loyalty through satisfaction as an intervening variable. (ix) To test and analyze the use of information technology which has a significant positive effect on customer loyalty through customer satisfaction as an intervening variable. (x) To test and analyze non-cash payments have a significant positive effect on customer loyalty through customer satisfaction as an intervening variable. This research uses descriptive and associative research methods, with a quantitative approach which aims to explain the influence of causal relationships between variables through hypothesis testing with a sample of 96 respondents, motor vehicle testing customers at the Mojokerto City Transportation Department. Data analysis in the research used path analysis followed by the help of the statistical application SPSS (Statistical Package for Social Sciences) Ver 26.0. The results of the study show that all the proposed hypotheses can be accepted with the most significant variable influencing Customer Satisfaction being Non-cash Payments.

Aspect-based sentiment analysis as fine-grained sentiment analysis problem, mainly aims to clarify the sentiment of different aspect. Previous fine-grained sentiment analysis methods usually used Recurrent Neural Networks as feature extractors, and achieved good results in sentiment analysis. However, they were limited to the sequential processing of sequence by RNN. Its training and operation efficiency are relatively low and time consuming. Aiming at this problem, combining the multi-head attention mechanism in Transformer, fusion model of convolution neural network and hierarchical attention coding network is proposed, and convolution neural network is used as feature encoder to improve the efficiency of model training. At the same time, multi-head attention is used to calculate the degree of relevance between aspect words and context, thus capturing emotional elements corresponding to entity words. Experiments on the SemEval2014 dataset have improved the training and inference efficiency of aspect-based sentiment analysis.

The purpose of this research is to analyze effect of service quality, online advertising, and learning innovation on customer satisfaction through brand image at Course Institutions in Tangerang City. This research approach uses quantitative research. The population of training participants in Tangerang City as many as 475 people. Based on the calculation, the number of samples to be taken is 221 people. Data analysis using inductive data analysis method. The results of the study show that service quality has a significant effect on brand image at Course Institutions in Tangerang City. Online advertising has no significant effect on brand image at Course Institutions in Tangerang City. Learning innovation has a significant effect on brand image at Course Institutions in Tangerang City. Service quality has a significant effect on customer satisfaction at Course Institutions in Tangerang City. Online advertising has no significant effect on customer satisfaction at Course Institutions in Tangerang City. Learning innovation has a significant effect on customer satisfaction at Course Institutions in Tangerang City. Brand image has a significant effect on customer satisfaction at Course Institutions in Tangerang City. Brand image is able to significantly moderate service quality on customer satisfaction at Course Institutions in Tangerang City. Brand image is not able to significantly moderate online advertising on customer satisfaction at Course Institutions in Tangerang City. Brand image is able to significantly moderate learning innovation on customer satisfaction at Course Institutions in Tangerang City.

The assessment of faculty teaching effectiveness plays a pivotal role in shaping educational practices and ensuring academic excellence. While numerical evaluations provide quantifiable measures of teaching effectiveness, textual comments offer valuable qualitative insights into the faculty's instructional methods and interactions. However, discrepancies between these evaluation formats may hinder the accurate assessment of faculty performance. This study aims to determine the alignment between the numerical and textual evaluations of faculty teaching effectiveness using fine-grained sentiment analysis and determine the relationship between numerical ratings and textual comments to identify patterns of consistency or divergence. A large dataset of anonymized BSCS students’ feedback is analyzed through sentiment analysis to extract sentiments expressed in the textual comments. The study results reveal a misalignment between students’ subjective perceptions expressed in the textual comments and the numerical ratings. This misalignment arises due to several factors, such as student communication style variations, individual interpretations of rating scales, or subjective biases. This study advances faculty evaluation methodologies and offers useful recommendations for organizations looking to improve assessment procedures as well as educational institutions that want to improve the precision and efficacy of their faculty evaluation systems for better faculty development and decision-making.

The purpose of this research are: (1) To know the quality of service, (2) To know the handling of complaints, (3) To know the satisfaction of customers' business credit, (4) To know the influence of service quality to customer satisfaction, 5) To know the effect of the handling of complaints on the satisfaction of business credit customers, (6) To determine the effect of service quality and handling complaints together on the satisfaction of customers' business credit. The subject of this research is customer loan business people in PT Bank Rakyat Indonesia Unit Rejoso Selatan in Nganjuk with 90 customer respondents. The method used in this research is quantitative approach. Research data obtained by questionnaire, observation, and documentation. The analysis technique used is multiple linear regression with F test and t test to test the research hypothesis. The results showed that, with the fulfillment of validity, reliability and classical assumption test, hypothesis t test proved the quality of service and handling complaints have a positive and significant impact on customer satisfaction of people's business credit. F test hypothesis proves that together, multiple regression model strengthen correlational correlation of service quality and handling of complaint to customer satisfaction of people's business credit.

The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.

Quality of service is an effort to fulfill needs coupled with consumer desires and the accuracy of the delivery method in order to meet customer expectations and satisfaction. Customer satisfaction is the level of one's feelings after comparing the performance (or results) that he perceives compared to his expectations. This study aims to analyze the quality of customer service on customer satisfaction and examine the effect of customer service quality on customer satisfaction at Bank Syari'ah Indonesia (BSI) Bengkalis Branch. The author uses a quantitative approach with descriptive analysis methods and statistical analysis. The population in this study as many as 6000 customers with the number of samples taken as many as 100 customers. While the data collection tool used is a questionnaire. The results of the research analysis show that the influence of customer service quality on customer satisfaction at Bank Syari'ah Indonesia (BSI) KCP Bengkalis shows a positive and significant effect on Customer Satisfaction with a contribution of 58% and the remaining 42% is influenced by other factors not examined

In the present age of rapid digital and technological progress, individuals spend a significant portion of their day connected to the Internet, actively expressing their thoughts and emotions on various online platforms. The analysis of these comments is important for detecting fundamental sentiments that lead to informed decisions across numerous sectors. Sentiment analysis (SA) classifies textual or spoken expressions as positive, negative, or neutral. Although several machine learning and NLP models are available for SA, accurately extracting and categorizing opinions remains challenging. This study compared seven state-of-the-art models-LSTM, BiLSTM, CNN, random forest, logistic regression, support vector machine (SVM), and naïve Bayeswith the BERT model to achieve precise sentiment classification. TF-IDF vectorization was performed before the SA. Using 11,323 annotated sentences from three domains, the models were assessed based on the F1- score, recall, precision, and accuracy. Each sentence was labeled to reflect either a positive or negative sentiment. The experimental results show that the BERT model performs best, achieving an accuracy of 99 % for Games data, 94 % for the comprehensive data, 90 % for the social networking data, and 97% for the productivity data. For all the data types, BERT outperformed all the state-of-the-art models considered for comparison. The findings offer practical insights for the mobile app industry by enhancing the understanding of user opinions through SA. This study contributes to real-world applications by guiding the effective selection of mobile apps based on fine-grained sentiment analysis.

Airline service quality, customer satisfaction, and repurchase Intention: Laotian air passengers’ perspective

This study conducted text mining and sentiment analysis on 800 Emirates Airline customer reviews from Skytrax to evaluate airline service quality.Through frequency analysis, TF-IDF analysis, and 3-gram analysis, key service elements were identified, while sentiment analysis and correlation analysis were used to analyze factors affecting service satisfaction.The analysis revealed strong correlations between customer satisfaction and ground service (0.55), cabin staff service (0.49), food service (0.48), and seat comfort (0.44).Notably, in the TF-IDF analysis, the 'business-class' keyword rose from 10th to 6th in frequency ranking, highlighting the importance of premium service.The 3-gram analysis identified transit service and special meal requirements as key areas of customer concern.Time series analysis indicated a decline in service satisfaction following the pandemic, suggesting the need for improvement.The theoretical significance of this study is that it presents a comprehensive analytical framework that combines text mining and sentiment analysis to analyze the service quality of FSC Airlines.It also made methodological contributions by capturing dynamic changes in service quality perception through time series-based sentiment analysis.From a practical perspective, the study proposed specific improvements in ground services, standardization of cabin crew service, enhancement of special meal services, and innovation in business class seating.While this research is expected to advance airline service quality management, it has limitations in analyzing only English reviews from a single platform.Future research should include various platforms and language groups.

Marketing strategy remains a critical driver of customer satisfaction and competitiveness in the banking industry globally. Despite this, Commercial banks in Kenya are yet to attain required customer satisfaction levels. This is evident in the low average customer satisfaction index (CSI) which dropped from 67% in 2011 with a downward trend to 60% in 2015 way below the Kenyan Banking industry benchmark of 77%. Studies on the relationship between service quality and customer satisfaction revealed both positive and negative results. These suggest that the relationship may be affected by other factors such as marketing mix strategy. Previous studies have not addressed the role of marketing mix strategy comprising of product, price, promotion, place, people, process and physical evidence in the relationship between service quality and customer satisfaction. The role of a moderating variable like marketing mix strategy can have a strong influence on the strength of the relationship thus it’s needed for the study. The main purpose of this study was to analyze the effect of marketing mix strategy on the relationship between service quality and customer satisfaction of commercial bank customers in Nairobi, Kenya.

Aim/Purpose: This study examined how service fairness and SERVQUAL dimensions influenced customer satisfaction and loyalty in mobile financial applications (MFAs) in Phnom Penh, Cambodia. As digital transformation reshapes financial services, understanding these factors is crucial for improving user experiences and fostering long-term engagement. Introduction/Background: Mobile financial applications (MFAs) have become an essential part of Cambodia’s urban financial ecosystem by providing accessible and cost-effective financial solutions. Despite their increasing usage, limited research exists on the factors influencing customer satisfaction and loyalty in this sector. This study aimed to bridge this gap by examining how service fairness dimensions (distributive, procedural, interpersonal, and informational) and SERVQUAL dimensions (reliability, responsiveness, assurance, and empathy) impacted customer satisfaction and loyalty. Methodology: A quantitative research methodology was employed to explore the relationships among service fairness, SERVQUAL factors, customer satisfaction, and loyalty. The study targeted individuals aged 18 and above who actively use MFAs in Phnom Penh. Due to the absence of precise population data, stratified purposive sampling was used to ensure diverse representation across Phnom Penh’s districts. Using Cochran’s formula with a 95% confidence level and a 5% margin of error, the study aimed for a sample size of 400 respondents, but 560 surveys were distributed to enhance reliability, yielding 470 valid responses. Data collection took place between May and July 2024 through online surveys and self-administered questionnaires distributed across various digital platforms. The survey instrument was structured into four key sections: demographic information, MFA usage experience, behavioral factors, and service fairness/SERVQUAL dimensions, which were measured on a five-point Likert scale. Survey items were adapted from prior studies to ensure measurement reliability and validity. The questionnaire was pretested with 30 respondents, and refinements were made to improve clarity and relevance. Advanced statistical techniques were used to analyze the data. Descriptive statistics provided an overview of respondent demographics and usage behavior. Cronbach’s Alpha was employed to assess reliability, while Confirmatory Factor Analysis (CFA) validated the construct dimensions. Structural Equation Modeling (SEM) was utilized to test the relationships among service fairness, service quality, customer satisfaction, and loyalty, with results presented through detailed statistical models and tables. Findings: The study revealed that both service fairness and SERVQUAL dimensions significantly influenced customer satisfaction, which, in turn, drives customer loyalty. Among service fairness dimensions, distributive and procedural fairness had the strongest effects on satisfaction, highlighting the importance of transparent processes and fair service outcomes. Customers responded positively to equitable resource allocation and well-structured procedural guidelines. Regarding SERVQUAL dimensions, responsiveness and assurance emerged as the most influential factors shaping customer satisfaction. Customers highly valued prompt responses to inquiries and the expertise and reliability of service providers. While empathy contributed to satisfaction, its impact was comparatively less pronounced. Customer satisfaction was found to mediate the relationships between service fairness, service quality, and loyalty. This underscored the necessity of fostering positive user experiences to enhance long-term customer retention and loyalty. The findings suggest that MFA providers should prioritize procedural fairness and responsiveness to optimize customer satisfaction and long-term engagement. Contribution/Impact on Society: This study provides empirical evidence of the role of service fairness and SERVQUAL factors in shaping customer satisfaction and loyalty in the Cambodian MFA market. While previous research has examined these factors in other industries, this study uniquely focused on MFAs in a developing Southeast Asian context. The findings emphasize the importance of fair service design and high-quality service delivery in building customer trust and loyalty. By implementing transparent service processes and prioritizing customer support quality, MFA providers can foster financial inclusion and promote sustainable business growth in emerging markets. Recommendations: For MFA providers, the study recommends emphasizing transparency, equitable service outcomes, and efficient customer support to enhance satisfaction and trust. Specifically, providers should: Improve distributive and procedural fairness by implementing clear, transparent, and fair resource allocation policies. Enhance responsiveness by ensuring customer inquiries are addressed promptly and effectively. Strengthen assurance through professional training programs that enhance staff expertise and reliability. Invest in customer service training to improve staff empathy and engagement with users. For researchers, further exploration into additional variables such as trust, security, and digital literacy could offer a more comprehensive understanding of MFA user behavior. Research Limitation: This study had several limitations. Its cross-sectional research design captured data at a single point in time, limiting the ability to infer causal relationships. Additionally, the study focused exclusively on Phnom Penh, restricting the generalizability of findings to other regions or countries. The reliance on self-reported data may have introduced potential bias, including social desirability bias. Future studies should adopt longitudinal designs and include a broader geographic scope to enhance research validity Future Research: Future studies should expand on this research by incorporating additional factors such as perceived security, trust, and digital literacy to provide deeper insights into customer satisfaction and loyalty in MFAs. Comparative studies across different regions or countries could reveal cultural differences in how service fairness and quality affect customer perceptions. Additionally, longitudinal studies tracking changes in satisfaction and loyalty over time would provide a dynamic perspective on user behavior and evolving service expectations.

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.