Customer Reviews Research Articles

KLASIFIKASI ULASAN PELANGGAN SHOPEE MALL TERHADAP E-COMMERCE PENJUALAN BAJU BATIK METODE NAÏVE BAYES

Batik, as a symbol of Indonesian culture, has become an everyday lifestyle and is not only limited to ceremonial occasions. In the era of globalization, the advancement of batik making has integrated its presence in various aspects of life, including in casual and informal clothing choices. E-commerce, such as Shopee Mall, provides a means for consumers to purchase batik without leaving the comfort of their homes or workplaces. Nonetheless, online shopping carries higher risks and uncertainties, emphasizing the importance of building consumer trust. Customer reviews, as a valuable source of information, are key in helping consumers make purchasing decisions. This research applies the Naïve Bayes method to classify customer reviews related to batik clothes from five sellers at Shopee Mall. Based on the results of the research and discussion carried out, it is concluded that in classifying customer reviews regarding the sale of batik clothes, using the Naive Bayes method on data from 5 sellers, where each seller has 350 reviews from Shopee Mall, the highest accuracy value is achieved by the seller Batik Prakasa, with an accuracy value of 81.90%, precision 81.90%, and recall 100%. Meanwhile, the most positive words were achieved by Batik Kanaya 1 seller, with a total of 520 positive words and 156 negative words, and achieved an accuracy of 77.19%, precision of 77.19%, and recall of 100%.

Effects of online customer reviews on sustainable clothing purchase intentions: The mediating role of perceived diagnosticity

AbstractOnline customer reviews play a vital role in evaluating product or service performance and serve as an important reference for other customers' purchase decisions. This study explores the influence of online customer reviews on sustainable clothing purchase intention through text mining and survey‐based methodologies. The results showed that online customer reviews focused on four dimensions: material ecology, clothing fashion, clothing functionality, and price reasonableness. Online customer reviews have a positive impact on consumers' sustainable purchase intentions, and perceived diagnosticity mediates the relationship between online customer reviews and purchase intentions. In addition, customers' prior sustainability knowledge moderates the relationship between material ecology, price reasonableness, and perceived diagnosticity. This study further enriches theoretical research on sustainable consumption within the context of online apparel retailing and helps sustainable apparel brands more accurately grasp the influencing variables of customers' sustainable consumption behaviours and formulate precise electronic word‐of‐mouth marketing strategies.

Brand Awareness Mediation: Analysis of Online Customer Reviews and Influencer Marketing on Local Perfume Purchase Decisions in E-Commerce

This research aims to determine the influence of online customer reviews and influencer marketing mediated by brand awareness on purchasing decisions for local perfumes in e-commerce. Quantitative methods were used in this research with a sampling technique using a purposive sampling method. The population in this study involved Gen Z and millennials with an age range of 17-40 years who were perfume customers in the city and district of Cirebon. The sample used was 150 respondents who met the research sample criteria. Data analysis in the research was processed using structural equation modeling (SEM-PLS) using the SmartPLS 4.1.0 application. Based on the results of the analysis of validity tests, reliability tests, discriminant validity, r-square tests, and path coefficients, results were obtained which stated that there was a positive and significant influence of each exogenous variable, namely Online Customer Reviews, Influencer Marketing on purchasing decisions. Furthermore, Brand Awareness can be a positive mediator in the correlation between Online Customer Reviews and Influencer Marketing which simultaneously influences Purchase Decision variables for local perfume products in e-commerce.

Semi-supervised topic representation through sentiment analysis and semantic networks

This paper proposes a novel approach to topic detection aimed at improving the semi-supervised clustering of customer reviews in the context of customers' services. The proposed methodology, named SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment (SMARTS) for Topic-Community Representation with Semantic Networks, combines semantic and sentiment analysis of words to derive topics related to positive and negative reviews of specific services. To achieve this, a semantic network of words is constructed based on word embedding semantic similarity to identify relationships between words used in the reviews. The resulting network is then used to derive the topics present in users' reviews, which are grouped by positive and negative sentiment based on words related to specific services. Clusters of words, obtained from the network's communities, are used to extract topics related to particular services and to improve the interpretation of users' assessments of those services. The proposed methodology is applied to tourism review data from Booking.com, and the results demonstrate the efficacy of the approach in enhancing the interpretability of the topics obtained by semi-supervised clustering. The methodology has the potential to provide valuable insights into the sentiment of customers toward tourism services, which could be utilized by service providers and decision-makers to enhance the quality of their services.

Objective and neutral summarization of customer reviews

Opinion mining aims to detect and extract relevant information from large quantity of customer reviews. Automatic opinion summarization then seeks to create a consensual point of view often oriented toward the main sentiment of clients to render their experience. Although factual information is valuable for companies to understand what works or not in their products, summarization approaches that convey objectivity, and constructive feedback from customer reviews have yet to be explored. We propose an adversarial multi-task learning model for document summarization to address this new issue. Our algorithm combines an autoencoder for document summarization with a gradient reversal layer to learn independent representations of subjective and sentiment-based material. We assess and compare our method on the Amazon product review dataset where we introduce an original evaluation dataset for objective summarization. We further completed the analysis with neutrality and objectivity metrics. This study demonstrates that the generated summaries carry out relevant and objective content but also emphasize the importance of various processes and layers in multi-task learners to control the information effectively.

Pengaruh Influencer dan Ulasan Pengguna Terhadap Keputusan Pembelian KOnsumen Pada Brand Skincare Avoskin di Media Sosial

This research focuses on the influence of influencers and customer reviews on Avoskin skincare purchases on social media, especially in digital marketing. The goal is to understand their role in consumer behavior, and explore effective marketing strategies to increase sales and loyalty. It aims to provide insight into the importance of influencers and customer reviews in Avoskin's digital marketing success. The research focused on Avoskin Skincare users, by collecting field data through questionnaires to 107 respondents who met the criteria. A quantitative descriptive approach was used to analyze the influence of beauty influencers on purchasing decisions. An online survey was used to explore respondents' opinions about influencers, interest in products, reviews, and purchase factors. Data analysis was conducted descriptively with a table of survey results.This study tested three hypotheses about the influence of influencers and user reviews on purchasing decisions. The first and second hypotheses are supported by previous research, showing the significant influence of influencers and user reviews. The results of the third hypothesis show that both jointly influence purchasing decisions with strong statistical significance. This confirms the important role of influencers and user reviews in shaping consumer purchasing decisions. The results of the study using IBM SPSS 29 confirmed that influencers and customer reviews have a positive and significant impact on purchasing decisions, encouraging consumers to purchase Avoskin skincare products in accordance with the hypothesis. However, this study has limitations in population and references, and only considers three variables. It is recommended that similar studies expand the population and consider additional variables for a more comprehensive analysis.

Network Structure of Online Customer Reviews and Online Hotel Reviews: A Systematic Literature Review

This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online customer reviews can benefit future hotel review research. Data collected from Web-of-Science and Scopus created an expanded network of 797 core articles and 19,374 citations to identify intellectual structures, developing trends, and future research gaps. This study offers a visual overview of journals, institutions, countries, research themes and authors to assess the overall directions hotels can take. It underscores the necessity for rigorous and relevant research amid the proliferation of online reviews and emphasises the imperative for academia to bridge the gap between theoretical insights and practical applications within the dynamic tourism industry. This study provides researchers and industry professionals with useful tools to understand and deal with the complexities of online reviews. It also highlights the important role these reviews play in shaping the future of tourism strategies.

Model Penyelesaian Masalah Keputusan Pembelian: Evaluasi Indikator Digital Marketing dan Online Customer Review di Toko Jangkar Kota Serang

This research is related to how the influence of digital marketing on purchasing decisions at the Toko Jangkar Kota Serang. The purpose of this study was to examine the effect of digital marketing on purchasing decisions partially, the effect of online customer reviews on purchasing decisions partially, the effect of digital marketing and online customer reviews on purchasing decisions simultaneously at the Toko Jangkar Kota Serang. Respondents in this study were 130 people. The type of sample in this study is non-probability with purposive sampling technique. The data collection method used is to use questionnaires and explained by quantitative methods. The results of this study indicate that digital marketing has a positive and significant effect on purchasing decisions, then online customer reviews also have a positive and significant effect on purchasing decisions. The two independent variables in this study are also empirically supported and simultaneously influence the dependent variable.

Exploration of the Role of Tiktok Content: Influencer Strategy, Affiliate Marketing, and Online Customer Reviews in Influencing Generation Z Purchasing Decisions at Shopee

This study aims to determine the effect of the role of influencers, affiliate marketing, and online customer reviews at Shopee through TikTok content on Generation Z purchasing decisions. This research uses a quantitative approach by collecting data through an online survey of 199 respondents of the Management Study Program students class of 2020. The data were analyzed using multiple regression methods to evaluate the relationship between the independent variables of influencer roles, affiliate marketing, and online customer reviews and the dependent variable (purchasing decisions). The analysis results show that the role of influencers, affiliate marketing, and online customer reviews have a positive and significant influence on Generation Z's purchasing decisions. The findings emphasize the importance of integrating marketing strategies through influencers, affiliate marketing, and online customer reviews in influencing Generation Z's purchasing decisions. This provides guidance for businesses to utilize e-commerce platforms such as Shopee and TikTok content as an effective means of communication to reach and influence the Generation Z market. This research makes a new contribution to the understanding of how the role of influencers, affiliate marketing and online customer reviews on Shopee, particularly through TikTok content, can influence Generation Z's purchasing decisions. This research adds insight into effective marketing strategies to capture the attention and influence the consumption behavior of the younger generation in the context of e-commerce platforms.

ENHANCING TECHNICIAN & PLUMBER HIRING STRATEGIES WITH PYTHON: A TRENDSETTING COMPUTER SCIENCE ANALYSIS

Abstract: The Online Platform for Technician and Plumber Hiring is a digital marketplace designed to streamline the process of connecting individuals and businesses with skilled technicians and plumbers for maintenance and repair services. The platform offers an intuitive interface accessible via web browsers and mobile applications, allowing users to search, compare, and hire technicians and plumbers based on their specific needs and preferences. Service providers create detailed profiles showcasing their qualifications, experience, areas of expertise, and customer reviews, allowing service seekers to evaluate the suitability of technicians and plumbers for their specific requirements. The platform also offers advanced search and filter functionality, allowing users to narrow down their options based on location, services offered, availability, pricing, and ratings. It facilitates seamless booking and scheduling of service appointments, allowing users to specify the date, time, and nature of the required services. Clear pricing and payment information are provided for each service, ensuring transparency and eliminating ambiguity for both service seekers and providers. After service completion, users can rate and provide feedback on the quality of work and customer service received from technicians and plumbers, maintaining accountability and encouraging continuous improvement within the service provider community. The platform also offers customer support services to address inquiries, resolve issues, and facilitate communication between parties if disputes arise during or after service delivery. Mediation mechanisms are in place to ensure fair resolution of conflicts. IndexTerms – HTML, CSS, AngularJs , NodeJs , Bootstrap, MySQL

An Integrated Approach for Amazon Electronic Products Reviews by Using Sentiment Analysis

In our routine life, we interact a wide range of products, and frequently browse through digital media platforms to access their quality. Although the accessibility of online platforms, consumers often find it challenging to swiftly judge the quality of products on the basis of customer reviews. To cope this situation, the study addresses this problem by suggesting a machine learning-based solution to categorize product reviews. For this, we employ various machine learning techniques, including Random Forest, Naïve Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) Classifier, and Bidirectional Encoder Representations from Transformers (BERT). In our model, we incorporate pre-processing methods for prepare the dataset for training and utilize feature extraction techniques such as TF-IDF and word2vec which are then applied to different classifiers to analyze the reviews. Moreover, we conduct this study by using the Amazon Electronics category dataset, it reveals that BERT outperforms other classifiers with a performance score of 0.8896. Therefore, this technique not only streamlines the procedure of evaluating product quality but also enhances the accuracy of review classification, giving a real-world solution for consumers and businesses alike.

Evaluating The Impact of Feature Extraction Techniques on Arabic Reviews Classification

With the advent of AI text-based tools and applications, the need to introduce and investigate word-processing tools has also been raised. NLP tools and techniques have developed rapidly for some languages, such as English. However, other languages, such as Arabic, still need to introduce more methods and techniques to provide more explanations. In this study, we present a sample to classify customer reviews which are written in Arabic. The data set (HARD) is used to be certified as a dataset for work. This study adopted four classifications in machine learning and deep learning (CNN, RNN, NB, LR). In addition, the texts were cleaned using data cleaning techniques, and the stemming technique was used, and three types of them were implemented (Khoja Stemmer, Snowball Stemmer, Thashaphyne Stemmer). Moreover, two methods of feature extraction were used (TF-IDF, N-gram). The results of the model provided several explanations. The best performance resulted from the use of (CNN+ Snowball Stemmer +N-gram) with accuracy (%93.5). The results of the model stated that some workbooks are sensitive to the use of different tools, and some accuracy performance can also be affected if there are different methods for extracting the features used. Either feature extraction has an impact on accuracy performance. The model also proved that colloquial Arabic could cause some limitations because different dialects can give different meanings across different regions or countries. The results of the study open the door to exploring other tools and methods to enrich natural Arabic language processing and contribute to the development of new applications that support Arabic content.

Legal Protection for E-commerce Business Actors regarding Consumer Reviews

Customer reviews in e-commerce play an important role in influencing the decisions made by customers. Before making a purchase, many people read positive customer reviews because they can offer useful information to potential buyers and increase their overall level of trust in the product. The purpose of this research is to analyse the good faith of business actors and how it impacts the law stipulated in the Consumer Protection Law. This research utilizes a normative legal research study whose findings show that consumers are not liable for criticism that harms businesses, according to the Consumer Protection Law (UUPK). In particular, Article 5a requires consumers to pay special attention to product descriptions listed in stores. In the e-commerce business world, business actors utilize the latest technology such as the internet to facilitate their business activities, yes; they get a number of benefits such as serving customers and making it easier to access distant information more quickly. If business actors receive negative reviews from consumers that do not match the facts so as to cause losses to business actors, the legal protection for business actors based on a positive legal perspective will be analysed. Based on the results of the analysis, it is found that giving negative reviews by consumers without a clear explanation and not according to the facts can harm business actors. This is considered a bad faith action by consumers.

Understanding the Factors Affecting Trust and Security in Online Transactions for Maid Service Websites

In today's digital landscape, trust and security hold significant importance, especially in the realm of online transactions, particularly on maid service websites. This paper delves into the complex factors that influence users' perceptions of trust and security when engaging with these platforms. Through a thorough examination of existing literature and empirical analysis, this research identifies the key factors that shape users' perceptions of trust and security during online transactions within the maid service industry. The study explores various aspects, including website design, encryption protocols, payment gateways, privacy policies, and customer reviews, among others, to understand their impact on users' trust and security perceptions. Additionally, it investigates how factors like customer demographics, previous online experiences, and awareness of cybersecurity threats contribute to shaping these perceptions. Furthermore, the research delves into the strategies and best practices adopted by maid service websites to enhance trust and security. This includes implementing secure payment gateways, transparent communication channels, and robust data protection measures. The insights gained from this study provide a deeper understanding of the dynamics influencing consumer trust and security in the context of maid service websites. These findings offer valuable implications for industry practitioners to improve trust-building strategies and strengthen security measures, ultimately creating a safer and more trustworthy online environment for users. Keywords: MaidIn, Maid Service, Maid Jobs, Maid Service near me, Maid Service Project, Maid Service full Stack Web Development

THE IMPACT OF ISLAMIC BUSINESS ETHICS, ONLINE CUSTOMER REVIEWS, AND ONLINE CUSTOMER RATINGS ON DECISIONS TO PURCHASE HALAL COSMETIC PRODUCTS THROUGH SHOPEE

Purchasing transactions have grown rapidly due to the presence of the Internet. The use of Internet facilities allows business activities to be carried out anywhere and at any time without having to meet the parties in the transaction physically. Business activity using the Internet is called electronic commerce (e-commerce). Currently, in Indonesia, the online shop or online marketplace trend is on the rise. According to Research and Markets, a market research platform, the market in Indonesia is potentially growing by 19.0% throughout 2022. Some of the most popular online shops are Shopee, Tokopedia, Lazada, Zalora, and so on. The study aims to find out the influence of Islamic business ethics, online customer reviews, and online customer ratings on the decision to buy halal cosmetics products through Shopee among students of Trunojoyo Madura University. The study used quantitative methods and purposive sampling with respondents of as many as 100 students. The results of the study showed that Islamic business ethics (X1), online customer review (X2), and online customer rating (X3) have a significant influence on the purchase decision of halal cosmetic products. The combination of all variables studied influences each other in determining the decision of students to purchase illegal cosmetic products.

The Influence of Brand Image and Online Customer Reviews Against Purchasing Decisions on the Marketplace Lazada

The Influence of Brand Image and Online Customer Reviews Against Purchasing Decisions on the Marketplace Lazada

Optimalisasi Tata Letak : Strategi Untuk Meningkatkan Kepuasan Pelanggan

This research conducted aims to explore the important aspects of office layout in a motorcycle dealer company which includes the objectives: (1) Identifying effective office layout strategies in motorcycle dealer companies, (2) Analyzing the effect of office layout on customer satisfaction, (3) Finding ways to optimize customer satisfaction of motorcycle dealer companies through office layout. The type of research is descriptive with a qualitative approach based on the results of direct observation at the Honda Naga Mas Motor Bandung office Jl. Soekarno Hatta No. 529, as well as observing customer reviews on Google regarding the layout of the company. The results showed that Honda Naga Mas Motor Bandung implements an efficient office layout strategy, because the applied office layout can optimize customer satisfaction through positive reviews related to the existing office layout in the company with evidenced of customers who feel more comfortable and easy to get service at the office because of the good office layout. These findings can provide insight and awareness to other motorcycle dealer companies regarding the importance of the right office layout strategy for optimal customer satisfaction.

Online review data analytics to explore factors affecting consumers’ airport recommendations

PurposeThe main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.Design/methodology/approach11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.FindingsThe results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.Practical implicationsBy encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.Originality/valueThis study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.

The price of short-term housing: A study of Airbnb on 26 regions in the United States

This study investigates the influence of customer review content and other attributes on Airbnb listing prices, using data from listings across 26 U.S. regions. By integrating sentiment analysis and topic modeling with hedonic and quantile regression models, this paper examines the significant role of electronic word-of-mouth in the short-term rental market, by studying how review sentiments and topics affect pricing strategies. The findings reveal that negative reviews have twice the impact on listing prices as positive reviews; and fourfold for lower-priced listings. Moreover, the results of topic modeling show that both positive and negative reviews related to facilities and location have a larger impact on listing prices. Additionally, the study looks at how the distance of listings from the city center and the timing of reviews affect pricing adjustments. The influence of reviews diminishes for listings located farther from the city center, indicating that Airbnb listing prices near the city center are more sensitive to changes in price determinants. The study also uncovers the existence of a lag effect in how reviews impact prices, showing that Airbnb hosts may not adjust prices as quickly as hotel owners. Overall, the study enriches existing literature by taking into account the review content and type of customer reviews, offering a more comprehensive understanding of short-term rental pricing.

Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn

Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products (n = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.