Search Results (94)

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure to labeled training data, making it valuable for handling emerging issues and dynamic complaint categories in finance. However, this task is particularly challenging, as financial complaint categories often overlap, requiring a deep understanding of nuanced language. In this study, we evaluate the zero-shot classification performance of leading LLMs and reasoning models, totaling 14 models. Specifically, we assess DeepSeek-V3, Gemini-2.0-Flash, Gemini-1.5-Pro, Anthropic’s Claude 3.5 and 3.7 Sonnet, Claude 3.5 Haiku, and OpenAI’s GPT-4o, GPT-4.5, and GPT-4o Mini, alongside reasoning models such as DeepSeek-R1, o1, and o3. Unlike traditional LLMs, reasoning models are specifically trained with reinforcement learning to exhibit advanced inferential capabilities, structured decision-making, and complex reasoning, making their application to text classification a groundbreaking advancement. The models were tasked with classifying consumer complaints submitted to the Consumer Financial Protection Bureau (CFPB) into five predefined financial classes based solely on complaint text. Performance was measured using accuracy, precision, recall, F1-score, and heatmaps to identify classification patterns. The findings highlight the strengths and limitations of both standard LLMs and reasoning models in financial text processing, providing valuable insights into their practical applications. By integrating reasoning models into classification workflows, organizations may enhance complaint resolution automation and improve customer service efficiency, marking a significant step forward in AI-driven financial text analysis. Full article

The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction. Full article

Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management. Full article

Background: The research presented here looks into ongoing problems with the package delivery services of a State parcel company, especially concerning damaged, wrongly delivered, late, and missing packages. These problems greatly affect customer satisfaction, so it is important to understand how they are connected. Methods: Three hundred and seventy-five customer complaints made between 2021 and 2023 were analyzed. Paniotto’s method was used to ensure that the study data accurately represented the situation. Pearson’s correlation coefficients helped find statistical links between different delivery problems. Results: The analysis revealed significant linkages among the core delivery issues. A strong positive correlation was found between damaged shipments and misdelivered shipments (r = 0.93835) and between prolonged delivery delays and lost goods (r = 0.9188). These findings suggest that addressing one issue may reduce the prevalence of others, given their tendency to coexist. Conclusions: The study emphasizes the necessity for the State parcel firm to execute a comprehensive strategy to improve the overall quality of parcel delivery services. Addressing concerns such as poor delivery and delays is critical since they are closely related to package damage and loss, which affects customer satisfaction. Full article

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law. Full article

Geosmin and 2-methylisoborneol (2-MIB) are secondary bacterial metabolites that create an earthy-musty taste and odour (T&O) in drinking water. Both compounds exhibit low odour thresholds and are the leading causes of customer complaints to water companies worldwide. Water companies must predict spikes in T&O concentrations early to intervene before these compounds reach the treatment works. Cyanobacteria are key producers of T&O in open waters, yet the influence of broader microbial and algal communities on cyanobacterial T&O events remains unclear. This study identified T&O risk indicator taxa using next-generation sequencing of bacterial (16S rRNA) and algal (rbcL) communities in three reservoirs in Wales, UK. Ordination analysis of these communities revealed clustering according to assigned T&O concentration levels, identifying T&O signature communities. Random Forest (RF) analyses accurately classified samples for high and low concentrations of geosmin and 2-MIB, demonstrating the biological consortium’s predictive power. Based on shared ecological traits of bacterial and algal taxa, we propose five categories corresponding to different magnitudes of T&O risk. Indicator taxa in T&O risk categories can then be used to predict T&O events, empowering water companies first to optimise treatment response and, importantly, to determine triggers before an event to evidence preventative intervention management. Full article

Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study is a discourse analysis that explores negative online guest complaints made to four luxury heritage hotels in Hong Kong that are classified as cultural heritage sites with rich interpersonal and historical values. We collected authentic guest complaints and responses from hotel managers from April 2012 to October 2022 in online travel forums, and then had GAI draft response letters on behalf of the hotel managers. Our total dataset was 65,539 words and consisted of three subcorpora: guest complaints (Text a of 115 complaints totaling 26,224 words), hotel manager responses (Text b of 115 response letters totaling 14,975 words), and AI-generated responses (Text c of 115 response letters totaling 24,340 words). This study used systemic functional linguistics to explore interpersonal meanings in texts; for example, appraisal resources, verb processes, and personal pronouns were compared between texts. First, we identified the most frequent words of the common themes across the three subcorpora and found significant differences in lexicogrammatical features between hotel managers and AI-generated responses using the log-likelihood ratio. The results suggest that AI-generated texts are able to provide a tailored and empathetic response to guests, but hotel managers may need to introduce some modifications, such as time indicators, sensory verbs used, and complimentary offers. This study explores the differences in word choices and communication strategies, which have implications and insights for the hospitality industry, especially luxury heritage hotels where caring and personalized customer service are considered important. Full article

Companies need to decrease service failures through service recovery and by managing customers’ emotions, which is important. Customer databases are vital to understand needs and service innovation. Utilizing data, companies can identify pain points and innovate services, reducing customer complaints. We examined 140 customer records from an auto maintenance company’s database and found 602 failure descriptions. The main complaints include maintenance performance, service attitude, and professionalism. Employee behavior accounted for most failures. Pain points in the process were the most common, followed by support, financial, and productivity issues. Such results suggest that service innovation is needed. Full article

Surveillance Response Systems (SRSs) have been deployed in Water Distribution Networks (WDNs) to detect various contamination events. However, in WDNs, some contaminants may remain undetected by an SRS due to the specificity of online water quality monitoring (OWQM). To overcome this limitation, OWQM can be supplemented with additional datasets to enhance the detection capabilities of the SRS framework. These additional datasets are based on health-seeking behaviors exhibited by consumers after consuming contaminated water as well as customer complaints. In this research, we implement a set of Bayesian networks in a clustered network to fuse these alternate datasets (simulated using an ABM due to the limited information associated with real events) with traditional OWQM to determine the likelihood of an ongoing contamination event. Full article

In today’s digital society, social networks such as Twitter are a preferred place for expressing one’s emotions, especially when they are negative. Despite a growing interest in the variety of linguistic realizations of commuters’ complaints, little attention has so far been paid to writers’ choices, especially when morphologically or syntactically simpler alternative formulations are available. A typical example is the “inference towards the antonym” triggered by the negation of contrary adjectives, an effect that is stronger for positive compared to negative adjectives. In the context of railway transport, a customer could use the negative statement The train is not clean instead of the corresponding affirmative sentence The train is dirty. It remains unclear, in our current state of knowledge, why online customers would prefer more complex constructions to voice their criticisms. Based on a large corpus of tweets sent to the French and Belgian national railway companies by their customers, I have semi-automatically extracted instances of not (very) + adjective (ADJ). Based on previous observations in the literature, I expected positive adjectives to be more frequently used in these negative environments compared to negative ones. As recent research demonstrates that one’s desire to save the interlocutor’s face is not necessarily the only reason why positive adjectives are used in linguistically negative environments, other motivations will also be considered. More precisely, I suggest that in a context where negativity is prevalent, customers using negated positive adjectives kill two birds with one stone: not only do they signal an issue with a product or a service, pointing to expectations that have not been met by the company, but they also mitigate the impact of their negative comments to the positive face of the service managers with whom they are interacting. By offering a quantitative, corpus-based analysis of negative constructions, complemented by a qualitative linguistic analysis of selected examples, this research sheds new light on users’ lexical choices in online negative customer feedback. Full article

Telecommunications firms face intense competition driven by rapid innovation and shifting consumer expectations. To remain competitive, companies are adopting Total Quality Management (TQM) to enhance customer satisfaction, corporate stability, and sustainability. This study examines TQM’s effects on customer satisfaction within Gulf Cooperation Council (GCC) countries’ telecommunications sector using secondary data from three firms’ quarterly reports (2019–2023). Descriptive, correlation, and regression analyses with STATA software reveal a significant increase in net promoter scores, indicating firms’ commitment to meeting evolving customer needs. Employee engagement and process management positively affect customer satisfaction, while continuous improvement practices and customer focus do not show a statistically significant influence. The research underscores TQM’s importance in fostering sustainable customer satisfaction by enabling telecom companies to adopt customer-centric strategies for achieving sustainable growth and long-term success. Aligning business processes with customer needs, especially in complaint handling, is crucial. The study advocates for implementing advanced customer relationship management (CRM) systems to better understand customer preferences. These strategic initiatives are vital for telecom firms to maintain competitiveness, enhance customer satisfaction, and contribute to the region’s overall economy. Full article

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features. Full article

The semiconductor manufacturing sector has contributed to the advancement of technical development in the sphere of industrial applications, but one crucial factor that cannot be overlooked is the evaluation of a machine’s state. Despite the presence of advanced equipment, data on their performances are not properly reviewed, resulting in a variety of concerns such as high rejection rates, lower production output, manufacturing overhead cost issues, and customer complaints. This study’s goal is to evaluate the performance of die attach machines made by a prominent subcontractor semiconductor manufacturing business in the Philippines; our findings will provide other organizations with important insights into the appropriate diagnosis of productivity difficulties via productivity metrics analyses. The study focuses on a specific type of die attach machine, with machine 10 showing to be the most troublesome, with an overall equipment effectiveness (OEE) rating of 43.57%. The Failure Mode and Effects Analysis (FMEA) identified that the primary reasons for the issue were idling, small stoppages, and breakdown loss resulting from loosened screws in the work holder. The risk priority number (RPN) was calculated to be 392, with a severity level of 7, an occurrence level of 7, and a detection level of 8. The findings provide new insight into the methods that should be included in the production process to boost efficiency and better suit the expectations of customers in a highly competitive market. Full article

This study reviewed different country studies and noted that complaints in Brazil are more concentrated in complaints about being attended to and receiving access to services, rather than about clinical quality and safety issues. This paper explores the possible explanations for these differences based on the institutional logics theory and which logics actors privilege, and how they may play out in the healthcare field. To accomplish this undertaking, this study makes use of the healthcare complaint categorization developed by Reader and colleagues, which has been used by various studies. Next, a set of studies about healthcare complaints in different countries was examined to analyze the issues most common in the complaints and compare this information with the Brazilian data. This study identified three explanations why complaints about medical errors seldom occur. One group of studies highlights the hardships of local health systems. Another focuses on patient behavior. Finally, the third kind focuses on the issue of power to determine health orientation. The studies about a lack of resources do not directly explain why fewer complaints about clinical quality occur, thus helping to stress the management issues. Patient behavior studies indicate that patients may be afraid to point out medical errors or may be unaware of the procedures of how to do so, suggesting that family logic is left out of the decisions in the field. The third group of work highlights the prominence of the medical professional logic, both in terms of regulation and medical exercise. Full article