Analysis Of Customer Reviews Research Articles

Towards Improving E-Commerce Customer Review Analysis for Arabic Language Opinion Mining

In recent years, the rapid development of Internet-related technologies has facilitated the widespread adoption of online purchasing as a convenient means of satisfying consumer needs. Conducting sentiment analysis on user reviews on e-commerce platforms can substantially improve customer satisfaction. In order to resolve this issue, we propose a novel model for sentiment analysis that employs hybrid deep learning ensembles, combining RNN and TreeLSTM with AraBERT as the word embedding. Our research concentrates on creating a hybrid deep-learning model to predict Arabic sentiment accurately. We employ deep learning models with various word embeddings, such as RNN, LSTM, BiLSTM, TreeLSTM, RNN-LSTM, RNN-BiLSTM, and RNN-TreeLSTM. Multiple open-access datasets are used to evaluate the efficacy of the proposed model, including the BRAD dataset, the ARD dataset, and merged datasets containing 610,600 items. The experimental findings indicate that our proposed model is well-suited for evaluating the sentiments expressed in Arabic texts. Our strategy starts with extracting features using the Arabert model, followed by developing and training five hybrid deep-learning models. We attained a significant accuracy improvement of 0.9409 when comparing our method to traditional and hybrid deep learning techniques. This demonstrates that our proposed model precisely analyzes sentiment. These findings are important for enhancing the comprehension of emotions conveyed in Arabic text and have practical implications for various applications, especially e-commerce. By accurately assessing sentiment, businesses can better comprehend customer preferences and enhance consumer satisfaction by enhancing their offerings.

Exploring the synergy of guided numeric and text analysis in e-commerce: a comprehensive investigation into univariate and multivariate distributions

This research adopts a holistic approach to analyze customer reviews in the e-commerce industry by utilizing a combined approach of numerical and text analysis. Specifically, this study integrates univariate, multivariate, and sentiment analysis to gain comprehensive insights into product preferences and customer satisfaction. The methodology includes a detailed examination of univariate distributions to uncover numerical trends in product ratings and preferences. Multivariate distributions are explored to understand the complex relationships between related variables. Sentiment analysis is performed using the Sentiment Intensity Analyzer to categorize reviews into positive, neutral, and negative sentiments. Additionally, N-gram analysis is applied to both recommended and non-recommended reviews to identify key themes, such as dissatisfaction with product size and satisfaction with fit. Logistic regression and naive Bayes models are employed to classify sentiment, with logistic regression achieving high accuracy on both training (91.3%) and validation data (89.2%). This research highlights the significant role of product recommendations as indicators of positive sentiment, while product ratings reveal the complexity in consumer judgment. The study contributes significantly to understanding the dynamics of customer reviews in the e-commerce industry, providing a solid foundation for smarter decision-making to improve customer experience and product quality.

Трансформация пространственной организации розничной торговли в Архангельске

The article compares the spatial organization of retail food trade in Arkhangelsk in 1988 and 2022 in order to identify the changes that have occurred in the post-Soviet period. Its transformation was facilitated by the closure of industrial enterprises, most of them were located in settlements in outskirts, a change in the solvency of residents, and a reduction in the number of potential buyers. The internal heterogenety of the conditions for the development of trade in different parts of the city, differences in the provision of retail outlets for residents, the prevailing trade formats that are typical for central and outlying areas are revealed. Compliance of the principle of equidistance of shops from residential buildings was checked. We also conducted a content analysis of customer reviews about various formats of retail outlets. We determined that in 2022 the number of stores in Arkhangelsk increased by 3 times compared to 1988. However, their distribution throughout the city is extremely heterogeneous. There is a clear competitive advantage of the central districts of the city (Oktyabrskiy, Lomonosovskiy), where the number of stores has increased by 4–5 times, over the outskirts, where the growth is less than 2 times (Maimaksanskiy, Isakogorskiy, Tsiglomenskiy districts). The most common form of trade organization in Arkhangelsk is a discount store, which is associated with the low solvency of the city population. Most of them belong to federal retailers. Their expansion in Arkhangelsk mainly affected the central districts and bedroom suburbs close to them. The least number of stores of federal and local retail chains has been opened in the outlying districts; there aren't any shops on the islands without bridges (Brevennik, Linskiy Priluk, Khabarka, Kegostrov). For outlying areas, the most common stores are those that do not belong to chain retailers, these are small pavilions and kiosks. Most customers’ reviews of such stores are negative and the overall rating of their work is lower than that of chain stores.

Unveiling the drivers of satisfaction in mobile trading: Contextual mining of retail investor experience through BERTopic and generative AI

The proliferation of mobile stock trading has introduced various apps with distinct features, emphasizing the need to understand users’ evaluations after adopting the service. This study explores the determinants of retail investors’ satisfaction with mobile stock trading services by employing an advanced textual analysis of customer reviews for four leading trading applications. We utilized Bidirectional Encoder Representations from Transformers (BERT) based Topic modeling (BERTopic modeling) to identify key topics within customer reviews and used the results as input for generative AI to discern the theme and sentiment of each topic. Based on Service Quality (SERVQUAL) theory, topics are categorized into key quality dimensions: functionality, usability, information quality, customer service, and system quality. Regression models were employed to assess the impact of the quality dimensions on investor satisfaction, revealing positive feedback on usability, information quality, and service quality as primary enhancers of satisfaction. In contrast, negative feedback on service quality, system quality, and functionality was identified as the primary inhibitor of satisfaction. This study explores how the influence of each quality dimension varies among different types of brokers (full-service vs. online-only brokerages). Finally, we propose a visualization tool called Topic Rating Impact and Frequency Analysis (TRIFA), which is designed to categorize topics based on their frequency of occurrence and impact on satisfaction. This tool aids in identifying the strengths and areas for improvement in services by effectively visualizing the results of text review analysis. This research not only deepens our understanding of the quality dimensions of mobile financial services but also offers valuable insights for service providers by suggesting predictive models that could help increase customer retention.

MATHEMATICAL MODELS AND ALGORITHMS IN LOGISTICS: REAL- WORLD APPLICATIONS AND AI IMPACT

Have you ever pondered the importance of mathematics in the field of logistics? Have you also reflected on whether a solid understanding of mathematical principles is crucial for achieving success in a logistics profession? This article explores the intersection of mathematics and logistics, shedding light on the indispensable role mathematics plays within the industry. By exploring practical applications, it provides insights into the mathematical concepts integral to the daily activities of logistics professionals. Whether aspiring to enter the field or carrying out further studies in logistics, this article offers a foundational understanding of mathematics' importance in logistics. In addition, we have also shown a case in which technological development and artificial intelligence are applied, where we have employed logistic regression, k-nearest neighbors, and random forest classifiers on data collected from Amazon to analyze customer reviews. Our results demonstrate that logistic regression outperformed the others, achieving an accuracy rate of 86.75%. This result is suggestive of the importance of this technique in building customer relations, particularly in their use in decision-making methods.

Sentiment Analysis of Customer Reviews on E-commerce Platforms: A Machine Learning Approach

Internet users are a huge segment of the consumer market, and businesses nowadays are trying to enter e-commerce, where customers leave reviews regarding products and services. Sentiment analysis is the process of extracting the customer's real feelings from the reviews of the product or services. This study compares logistic regression, naive Bayes, neural networks, and support vector machine algorithms for sentiment analysis and finds the best-performing classifiers among them. This applied study evaluates the classifiers using accuracy, precision, recall, and F1-score metrics. The dataset was taken from the E-Commence website, on which NLP and other classifiers are employed. The results show that the Naive Bayes model, with 94% accuracy, outperforms the different classifiers, where Logistic Regression and Neural Networks are at a similar level of 93%. In comparison, the SVM gave us an average of about 92%. This study suggests the significance of continuously updating sentiment analysis systems to maintain accuracy and relevance. Real-time sentiment analysis tools are a good technique for any text mining work that can help companies address customer problems based on immediate feedback and improve their products.

Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges.

In recent years, e-commerce platforms have become popular and transformed the way people buy and sell goods. People are rapidly adopting Internet shopping due to the convenience of purchasing from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses increasingly rely on online reviews to assess and improve the quality of products. Existing literature uses natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis and opinion mining, review analysis and management, customer experience and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey also addresses open issues, providing future directions in analyzing online customer reviews.

Guided Diverse Concept Miner (GDCM): Uncovering Relevant Constructs for Managerial Insights from Text

The Guided Diverse Concept Miner (GDCM) is an innovative deep learning algorithm tailored for the extraction of managerially relevant concepts from textual data, emphasizing the autonomy in discovering insights without predefined labels or guidance. This tool stands out by embedding words, documents, and concepts within the same vector space, which simplifies the interpretation of unearthed concepts and ensures their alignment with managerial outcomes. Central to GDCM’s methodology is its capacity to focus on concepts that are highly correlated with user-specified managerial outcomes, termed guiding variables, thereby enhancing the relevance and application of extracted insights in decision-making processes. The algorithm’s design inherently promotes the diversity of the recovered concepts, ensuring a broad spectrum of insights. Through practical application in analyzing customer reviews related to online purchases, GDCM not only identified key concepts influencing conversion rates but also validated its findings against established theories and prior causal research. This validation underscores GDCM’s utility in generating actionable, diverse insights tailored to specific managerial contexts, marking a significant advancement in how businesses leverage textual data for strategic decisions.

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

A Study on Maybelline Products: A Review on Customer Satisfaction

This research aims to conduct a comprehensive study on Maybelline products, a renowned brand in the beauty and cosmetics industry, with a specific focus on understanding customer satisfaction. Maybelline, a subsidiary of L'Oréal, has established itself as a global leader in offering a diverse range of makeup and beauty products. Employing a mixed-methods research design, combining online reviews, surveys, and interviews, this study seeks to explore the perceptions, preferences, and overall satisfaction levels of consumers using Maybelline products. Key objectives include analyzing customer reviews on various platforms, assessing the factors influencing customer satisfaction, identifying popular and well-received products, and understanding customer expectations from the brand. The study also aims to uncover any challenges or areas for improvement as perceived by customers. The findings of this research are expected to contribute valuable insights to Maybelline and the broader beauty industry, aiding in the enhancement of product offerings, marketing strategies, and overall customer satisfaction.

Analysis of customer reviews with an improved VADER lexicon classifier

BackgroundThe importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint.AimThis study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary.MethodologyThe proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier.ResultCompared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification.OutcomeProduct designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.

An integrated framework for importance-performance analysis of product attributes and validation from online reviews and maintenance records

Abstract Importance-performance analysis (IPA) is widely used for needs analysis, product positioning, and strategic planning in product design. Previous research on IPA often employs single-source data such as customer surveys or online reviews with unavoidable subjective bias. In contrast, product maintenance records provide objective information on product quality and failure patterns, which can be cross-validated with customers’ personal experiences from surveys or online reviews. In this paper, we propose an integrated framework for conducting IPA from online reviews and product maintenance records jointly. An attribute-keyword dictionary is first established using keyword extraction and clustering methods. Then, semantic groups, including product attributes and associated descriptions, are extracted using dependency parsing analysis. The sentiment scores of identified product attributes are determined by a voting mechanism using two pre-trained sentiment analysis models. The importance of product attributes in IPA is estimated from the impact of sentiments of each product attribute on product ratings with the extreme gradient boosting (XGBoost) model, while the performance is estimated from the sentiment scores of online reviews or the quality statistics from product maintenance records. In addition, we propose two methods to validate the IPA results, in which the IPA results are compared with the actual product improvements on the market or compared with the analysis of customer reviews from different time periods, respectively. The validated IPA results from online reviews and maintenance records are then integrated to obtain a more comprehensive understanding of customer needs. A case study of passenger vehicles is used to demonstrate the framework. The proposed framework enables automatic data processing and can support companies in making efficient design decisions with more comprehensive perspectives from multisource data.

Sentiment analysis of customer reviews for Algerian dialect using the DziriBERT model

Sentiment analysis of customer reviews for Algerian dialect using the DziriBERT model

Sentiment analysis of customer reviews for Algerian dialect using DziriBERT model

Sentiment analysis of customer reviews for Algerian dialect using DziriBERT model

Recommend or not: A comparative analysis of customer reviews to uncover factors influencing explicit online recommendation behavior in peer-to-peer accommodation

In the fiercely competitive landscape of the peer-to-peer accommodation platform Airbnb, understanding customers’ online recommendation behavior is essential for optimizing competitiveness and harnessing the potential of electronic word-of-mouth. This study aims to provide comprehensive insights into the factors driving customers’ issuance of positive or negative recommendations based on their experiences. Leveraging 10,101 online reviews from Airbnb users, we employ the Structural Topic Model to determine latent topics relating to customers’ recommendation intentions. This study reveals that positive recommendations are primarily influenced by hedonic values tied to the overall experience, including crucial aspects such as convenience for exploring points of interest, local authenticity, social interaction, and emotional fulfillment. These findings highlight the significance of providing memorable and enriching experiences to elicit positive reviews from customers. On the other hand, negative recommendations are closely linked to utilitarian values focused on practical concerns such as check-in procedures, cleanliness, comfortableness, and amenities. Understanding these utilitarian factors is vital for hosts and platform administrators to improve service quality and address potential issues that may lead to negative reviews. Furthermore, this study emphasizes the significant role of hosts in shaping both positive and negative recommendations. This research contributes valuable insights to the field of peer-to-peer accommodation, highlighting the significance of various factors influencing customers’ recommendation behavior.

A Novel Customer Review Analysis System Based on Balanced Deep Review and Rating Differences in User Preference

A Novel Customer Review Analysis System Based on Balanced Deep Review and Rating Differences in User Preference

Improvement of marketing tools for ensuring the development of the innovative potential of aviation enterprises

The aim of the article. The purpose of the study is to improve the marketing toolkit for ensuring the development of the innovative potential of aviation enterprises. The results of the analyses. It has been established that traditional marketing is no longer sufficient to target the modern consumer, as his needs and requests are changeable, flexible, shaped by the influence of the global competitive environment in the air transportation market. Analysis of consumer behavior and permanent communication between airlines and customers is an indispensable attribute of marketing activities. The improvement of marketing tools for ensuring the development of the innovative potential of airlines emphasizes the need to increase the volume of initial marketing data for the formation of innovative policies, the collection, processing and interpretation of which is impossible without digital marketing tools based on artificial intelligence and machine learning in real time. Adaptation of products and services of air carriers to new market demands in conditions of quite strong competition requires the introduction of progressive marketing tools and technologies, which includes Growth-Hacking. At the stages of development of an innovative offer, Growth-Hacking is aimed at conducting experiments to find the correspondence between new market value and consumer needs. At this transition of a product innovation from concept to marketing and sales, Growth-Hacking tools intersect with adaptive marketing tools in targeting the airline's efforts to attract customers by meeting their needs. If adaptive marketing at this stage operates with general tools for assessing the target audience, strengthening customer relationships, developing a competitive advantage strategy, then Growth-Hacking is aimed at experimental conversion testing through chatbot tools, gamification, conducting usability tests with customers, customer surveys, analysis of customer reviews, use of heat maps. Collecting data and feedback from real customers at this stage helps inform experimentation to find an innovative solution. The creation of a comprehensive system of marketing tools to ensure the development of innovative potential is of crucial importance for the airline's competitiveness, allowing to offer customers more innovative and improved products and services relevant to their needs. Conclusions and direction for further research. The marketing toolkit for ensuring the development of the innovative potential of airlines has been improved, which is proposed to be formed on the basis of the synthesis of digital marketing, adaptive marketing and Growth-Hacking-marketing. The proposed approach covers all stages of the innovation process with marketing tools, starting from the generation of an innovative idea to the implementation of innovative products and services of airlines, therefore its implementation into a functional model of the development of innovative potential is rational. The advantage of the author's vision is the scientifically based marketing tools proposal of the relevance of the innovative idea to the needs and requests of consumers (adaptive marketing based on big data), its quick and cost-effective implementation (Growth-Hacking approach). With the perspective of further research, it is expedient to determine the justification of approaches to the strategic focus of the marketing concept on the development of the innovative potential of aviation enterprises.

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.

A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

Enhancing Customer Satisfaction in the Airline Industry: A Case Study of Delta Airlines

Customer satisfaction plays an increasingly important role as it drives customer loyalty, fosters positive brand image, and provides a competitive advantage. Airlines that prioritize customer satisfaction are better positioned to thrive in a highly competitive market, attract and retain customers, and achieve long-term success. This paper focuses on enhancing customer satisfaction in the airline industry, with a specific case study of Delta Airlines. The study utilizes sentiment analysis techniques to analyze customer reviews from Skytrax and conducts a comprehensive SWOT analysis. Based on the analysis, targeted suggestions are provided to Delta, including leveraging technology to reduce the negative impact of delays, establishing uniform service standards, broadening global networks and partnerships, committing to sustainability and ESG initiatives, and ensuring quick responses to policy changes. However, the study acknowledges limitations such as reliance on a single data platform, the subjective nature of sentiment analysis, and the oversimplification inherent in SWOT analysis.In all, this study helps Delta to improve its customer satisfaction and contributes an research example to explore customer satisfaction in the airline industry.