Online Reviews Research Articles

Rural Medicine is not Permanently Paralyzed: A Systematic Online Review to Mitigate Misdiagnoses of Rare Neurological Syndromes such as Guillain-Barre Syndrome in Areas of Limited Healthcare Accessibility

Rural Medicine is not Permanently Paralyzed: A Systematic Online Review to Mitigate Misdiagnoses of Rare Neurological Syndromes such as Guillain-Barre Syndrome in Areas of Limited Healthcare Accessibility

Gender rating gap in online reviews.

Although online reviews are used by many people to make decisions, these reviews may be biased. On the basis of 1.2 billion observations across five leading online review platforms and two lab studies (n = 1,172 and n = 1,165; US respondents fluent in English), we provide evidence for a consistent and systematic gender rating gap: women's mean online review ratings are significantly more favourable than men's. We suggest that although men and women, on average, generally do not differ in their 'real' attitudes, their ratings do differ when it comes to online reviews. Our lab studies revealed that such differences are due to gender differences in the propensity to share negative attitudes online, possibly due to women's greater concern about social consequences. Our findings highlight the need for societal change to create conditions in which people, particularly women, feel comfortable publicly expressing genuine attitudes, especially in cases of dissatisfaction, without being concerned about the consequences.

How loud is consumer voice in product deletion decisions? Retail analytic insights

This study examines the role of online consumer reviews in product deletion decisions. Building upon product portfolio management theory and the signaling theory, we integrate consumer voice, represented by online consumer review behavior into organizational voice—strategic product deletion decision-making. We empirically evaluated the relationship between online consumer reviews and organizational product deletion decisions using text mining and logistic regression models. The results indicate consumer online review characteristics are significant factors for product deletion decisions. Findings suggest that products with lower review attribute ratings, or those with textual review content less pertinent to the core performance attributes, are more likely to be deleted. The linguistic retail analytic characteristics of online reviews also provide insights for product deletion decisions. Products with reviews having higher subjectivity, shorter length, and lower readability are more likely to be deleted. Pre-purchase consumer voice—the perceived helpfulness or unhelpfulness of online reviews also impacts product deletion decisions. A general conclusion is that online reviews can provide important retail analytics for smarter retail operations planning at the strategic and tactical levels when it comes to product planning and portfolio management through product deletion.

An FMEA decision support model for hotel risk assessment based on the risk tolerance of multi-type travelers and online reviews

As a reliability management tool, failure mode and effect analysis (FMEA) is widely used to solve the problem of risk assessment in all trades and professions. Considering the opinions interaction and the diversity of risk tolerance of experts, this paper constructs a decision-support model of FMEA based on dynamic interaction of opinions and risk tolerance. Firstly, word frequency statistics and Word2Vec analysis are applied on 14,194 online reviews collected from TripAdvisor.com to identify major risk factors. Secondly, the consensus opinion of risk is reached through the dynamic interaction of opinions. Furthermore, the weight problem of risk factors is solved by a linear programming model with the goal of minimum individual regret, and the priority of risk is determined by an improved TODIM method considering risk tolerance. Through simulation experiments, sensitivity analysis and comparative analysis, the effectiveness and superiority of the proposed method are verified, and the theoretical research of FMEA method is enriched. This study provides a direction for the future improvement of hotel services based on understanding the preferences and interactions of multi-type travelers.

Destabilising the home: place making, dark tourism and the spectral

Ghosts, hauntings and the spectral are intrinsically linked to sites of dark tourism. Supernatural stories commonly emerge in places connected with tragedy, death and the macabre, forming spectral geographies in which the past and present intertwine. Visiting places of supernatural significance has long been of interest to tourists (Holmes and Inglis, 2004), however, in the last two decades a considerable ‘ghost-hunting’ subculture has emerged alongside commercial events and tourist attractions dedicated to engaging visitors with spectral possibilities (Eaton, 2020; Ironside, 2018). As a result, places associated with ghost folklore including private homes, hotels, and heritage buildings, have become popular travel destinations due to their supernatural associations. Film and television media are often credited with the popularisation of haunted places (Edwards, 2019; Hill, 2010); however, more recently web-based and social media platforms have become spaces for promoting, reporting, and sharing paranormal experiences. In this paper, we explore the construction of spectral geographies, specifically haunted houses, through online narratives. By drawing upon textual analysis of both marketing and online reviews we analyse two cases, 30 East Drive and The Ancient Ram Inn, and explore how these private homes have been transformed into sites of dark tourism through digital storytelling and discourse. Through our analysis we consider how personal experience, intertextuality, and uncanny signifiers contribute to a form of placemaking through digital media and storytelling (Halegoua & Polson, 2021), experiential consumerism (Pine & Gilmore, 2011) and an evolving spiritual quest culture (Eaton, 2015).

The power of electronic Word of Mouth in inducing adoption of emerging technologies

This study examines the influence of electronic Word-of-Mouth (eWOM) on consumer expectations and intentions to adopt emerging technologies, specifically focusing on cryptocurrency payment methods. Employing the Elaboration Likelihood Model (ELM), the research utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (PLS-MGA) to analyze data from a diverse sample of 505 respondents sourced from MTurk. The findings reveal that the quality, consistency, and volume of eWOM significantly shape consumer expectations. Notably, the two-sidedness of online reviews does not have any substantial impact on both expectations and adoption behaviors toward cryptocurrency payment methods. Furthermore, factors such as the time spent online, and the frequency of online shopping were found to partially moderate the effects of eWOM on adoption behavior. This research contributes pioneering insights into the role of eWOM in influencing consumer attitudes towards cutting-edge technologies, extending existing knowledge beyond traditional consumer decisions to include technological adoption, particularly in digital finance. This offers valuable implications for technology firms and digital marketers aiming to harness eWOM to promote new technological solutions.

Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM

Aspect category detection (ACD) is a pivotal subtask within the field of sentiment analysis in natural language processing, aiming to identify implicit aspect category information in online review texts. In real-world scenarios of online review category detection tasks, data imbalance often arises, leading to skewed distributions among distinct review categories. This phenomenon poses substantial challenges for accurately recognizing minority categories through modeling. To address this, we propose a method for detecting imbalanced aspect categories by combining the selective synthetic over-sampling (Select-SMOTE) algorithm with the light gradient boosting machine (LightGBM). Our approach commences with text data representation through features, followed by a strategy involving joint sample partitioning and boundary optimization within the feature space to generate minority class samples. This partitioning strategy aligns generated data more closely with the original distribution, while the boundary optimization module enhances classification performance by eliminating samples near boundaries. Subsequently, the balanced dataset is input to the LGB model, enabling the extraction of aspect category information through parameter optimization and class weight assignment. Finally, our method is evaluated using the SemEval and SentiHood datasets and compared with prevailing sampling methods and classification models. Empirical results manifestly demonstrate the method’s superiority across diverse metrics, reflecting robustness and effective mitigation of imbalanced data challenges in ACD.

From Likes To Purchases: The Role Of Social Media In Consumer Buying Patterns

This study explores the influence of social media on consumer buying behavior through a quantitative analysis involving 90 respondents, employing structured surveys for data collection. The findings reveal that WhatsApp and Instagram are the most used platforms, each utilized by 76.7% of participants. Significant factors impacting purchasing decisions include product recommendations from friends and family (41.1%) and online reviews (38.9%), with 53.3% of respondents acknowledging that social media has affected their spending habits. Furthermore, 64.4% reported making purchases based solely on social media recommendations. While 37.8% recognized the influence of social media influencers, their overall impact on purchasing behavior is deemed moderate. These findings highlight the critical role of social media as a strategic tool for enhancing consumer engagement, providing essential insights for marketers to develop effective strategies that leverage these platforms. The study emphasizes the necessity for brands to navigate the complexities of social media to maximize their influence on consumer behavior and adapt to the evolving digital marketplace

A knowledge graph-enabled multi-domain mapping approach supporting product rapid design: A case study of new energy vehicles

In the context of mass personalization, the dynamic and erratic nature of customer requirements (CRs) is increasingly pronounced. Amidst intensifying market competition, enterprises are compelled to swiftly adjust their products to satisfy the ever-changing CRs. However, the complicated design processes often result in prolonged product update and iteration cycles. Thus, it is urgent for enterprises to promote product rapid design, in which, it is essential to map CRs to functional requirements (FRs), then to design parameters (DPs). Traditional mapping methods are time-consuming and labor-intensive, struggle to keep pace with the ever-changing CRs. Moreover, enhancing the efficiency of design knowledge management and reuse is imperative for achieving product rapid design. Considering the strengths of knowledge graphs (KGs) in knowledge management and mining, we establish a multi-domain product design KG (MPDKG) including a CR domain, a FR domain, and a DP domain, and facilitate the mapping process by constructing mapping relations between domains. Firstly, CR entities in the CR domain are extracted through a triple-task cascaded CRs identification model from online reviews and complaints. Secondly, FR entities are identified from product functional lists and instructions, and the Sentence-Bert model is employed to establish the mapping relations between CR entities and FR entities. Subsequently, a corpus comprising product design knowledge is constructed and a cross-domain joint entity relation extraction model is developed to extract DP entities and construct their mapping relations with FR entities. Lastly, the multi-domain mapping of BYD Qin is implemented as a case study to elucidate the practical utility and effectiveness of the proposed methodology.

PERANAN FINANCIAL TEKNOLOGI DIGITAL MENGGUNAKAN MARKET PLACE SERTA ONLINE CUSTOMER REVIEW TERHADAP KEPUTUSAN PEMBELIAN DAMPAKNYA PADA LOYALITAS KONSUMEN (Studi Empiris Pengguna Marketplace UMKM Kota Tangerang Selatan)

The role of financial technology is considered to be at the forefront of digital market developers through market places to increase the income of MSMEs in South Tangerang City. The objectives of this research are 1.) To test and analyze whether financial technology influences purchasing decisions 2). analyzing Online Customer Reviews on purchasing decisions 3). testing financial technology and Online Customer Reviews simultaneously influence purchasing decisions 4). To test and analyze whether purchasing decisions have an impact on consumer loyalty in South Tangerang City MSMEs. The method used in this research is a quantitative method. Primary data collection was carried out by distributing questionnaires to MSMEs using the market place. Population of all business actors in South Tangerang City. The sample uses questionnaires and interviews. The sampling technique used is a random sampling technique. The analysis tool in this research is SEM (Structural Equation Modeling) processed through the AMOS (Analysis of Moment Structure) program. The research results can be concluded as follows: (1). There is a partial effect of financial technology on purchasing decisions using a value of 52.5% obtained from the determinant test. (2) Online Customer Reviews influence purchasing decisions (3). financial technology and Online Customer Reviews simultaneously influence purchasing decisions (4). Purchasing Decisions that impact Consumer Loyalty.

Home is best: Review source and cross-border online shopping

Electronic word of mouth (eWOM) has attracted considerable research interest in the past two decades. This paper revisits the impact of eWOM in the context of international business. Using review data scraped from AliExpress, a cross-border e-commerce platform, we show that the impact of online reviews is related to the identity of reviewers. Consumers are most affected by reviews from their home country, followed by reviews from neighboring countries, while they are not affected by reviews from strangers. This confirms the existence of home bias in the consumption of review information. In addition, the bias is more profound among consumers from countries with higher levels of uncertainty avoidance and trust. Our study is among the first to investigate eWOM in digitized international business. By discovering and reporting how consumers react to reviews with different identities, we offer actionable implications for digital platforms to improve the effectiveness of online reviews.

Patient perceptions of lisdexamfetamine as a treatment for binge eating disorder: An exploratory qualitative and quantitative analysis

Lisdexamfetamine (LDX) is the only medication to have gained FDA approval for the treatment of binge eating disorder (BED). LDX treatment is generally effective at reducing binge eating symptoms but is associated with several unwanted side effects. How BED patients perceive the therapeutic benefits vs. associated side effects of LDX has not been explored. We carried out thematic analysis of 111 online reviews posted to the website Drugs.com by persons prescribed LDX to treat BED. We also explored how qualitative themes were associated with perceptions of efficacy on a quantitative (1-10 scale) scale. Themes associated with higher efficacy ratings included improved binge eating outcomes, enhanced focus/concentration, as well as weight loss (χ2 tests, p’s<0.05). Lower efficacy ratings were associated with themes that included development of physiological tolerance to LDX, insomnia, return of binge eating in the evening, loss of energy in the afternoon/evening (‘crashing’), and weight gain (χ2 tests, p’s<0.05). Limitations of the study include representativeness of the data and self-reported BED diagnosis. Together, these data provide novel insights into individual experiences with LDX as a treatment for BED and their association with perceived efficacy. The causal nature of these relationships should be tested in future studies, as well as any implications for medication adherence.

A least-squares framework for developing interval type-2 fuzzy semantics

The developing of IT2 fuzzy semantics is critical for computing with words (CWW), but exiting approaches lack flexibility and fail to adapt user’s diversified demands. The study proposes a least-squares framework for designing CWW encoders that construct interval type-2 fuzzy sets (IT2 FSs) to represent the semantic meanings of linguistic words. In the least-squares framework, an CWW encoder is characterized by two elements: an intra-uncertain semantic mapping and an inter-uncertain semantic family. The intra-uncertain semantic mapping transforms data intervals into type-1 fuzzy sets (T1 FSs), then an optimal IT2 FS is derived from the inter-uncertain semantic family using the least-squares method. Furthermore, several intra-uncertain semantic mappings are introduced, and a compatibility measure is defined to facilitate model selection. The least-squares framework benefits from the flexible selection of intra-uncertain semantic mappings and least-squares optimization-based construction of IT2 FSs. In experiments, the least-squares framework is applied to handle real-world online survey data and the large-scale online review data set of a Chinese life service review site, Dianping.com. Compared to the enhanced interval approach and the Hao-Mendel approach, the least-squares framework shows its favorable efficiency in experiments and statistical tests, and adapts to user-defined intra- and inter-uncertain semantic families.

Tourist Preferences for Hotels and Accommodation in Samosir Regency Based on Online Review

Purpose: This study aims to uncover tourist preferences for various accommodation types, including hotels in Samosir Regency. These preferences include attractiveness, facilities, and hospitality. Research methods: This study uses a qualitative approach through secondary research with data collection from online reviews. The sample consists of 50 accommodations in Samosir Regency based on Google Reviews with ratings &gt; 3.0 and more than 30 reviewers. Data was collected by searching on google.com/travel with a total search result or population of 118 items, and a sample of 50 was obtained using the purposive method with the provisions of the Google Review Summary where the Rating Review was &gt; 3.0; and the Number of Reviews &gt;30 reviewers. Data were analyzed based on the Component Review using a Likert scale for 3 elements of the tourism industry, namely (1) Attraction that consists of atmosphere, location, and property; (2) Facilities consisting of Amenities, Bathrooms, and Bedroom; and (3) Hospitality which consists of Service, Cleanliness, and Food. Result and discussion: The analysis result revealed that the element of attraction shows a very satisfying preference from tourists. In second place is a satisfying preference for hospitality elements. Meanwhile, the facilities element shows the lowest preference or is unsatisfactory. This description of preferences shows that among the elements of tourism, improving the quality of hotel/accommodation facilities can be the biggest priority in efforts to develop tourism in Samosir Regency in providing high satisfaction and return visits. Implication: As a tourist destination, Samosir Regency, which relies on the Lake Toba area, has experienced an increase in tourist visits from year to year, both foreign and local tourists. This is facilitated by the general public's access to information and locations using information technology and the Internet.

Analysis of Historical Park Image Perception Based on Online Reviews from a Sustainable Development Perspective

Analysis of Historical Park Image Perception Based on Online Reviews from a Sustainable Development Perspective

Harnessing the Power of Web Scraping and Machine Learning to Uncover Customer Empathy from Online Reviews

In today's information-driven world, companies need to grasp customer sentiment and pinpoint key product features to make informed choices and improve customer satisfaction. The abundance of online customer feedback provides businesses with a rich source of information about customer perceptions and preferences. Yet manual analysis of large volumes of reviews is a tedious and resource-intensive task. It describes a web scraping application that sorts reviews into positive, negative, or neutral sentiments that businesses can use for insight. By assigning weights to reviews based on perceived impact, the approach offers a nuanced understanding of customer opinions. Experimental results demonstrate its effectiveness in accurately categorizing and analysing reviews, distinguishing between genuine and fake feedback. Additionally, AI-powered examination of product reviews employs NLP methods to extract important information from customer feedback, identifying particular elements that customers like or find troubling. After the successful identification of gain points, they can be used to improvise product development and marketing. So, product-specific focused crawler is developed to extract customer reviews from the most trusted websites. As a side chain, this can also be used as a tool for analysing the competitors’ products to understand their pain as well as gain points. The approach can uncover the most discussed aspects of products, enabling businesses to better understand customer perceptions and preferences to make better decisions thereby saving time and money.

Aspect based sentiment analysis of Twitter mobile phone reviews using LSTM and Convolutional Neural Network

The proliferation of online shopping has led to a surge in product reviews, providing valuable information to consumers. However, the overwhelming volume and subjective nature of these reviews make it difficult to assess product performance accurately. We propose a machine learning-based system that extracts sentiment from online reviews to address this challenge. Our system effectively identifies positive, negative, and neutral sentiments and classifies sentiment for specific product aspects. By offering concise and clear information, our system empowers consumers to make informed purchasing decisions and assists manufacturers in improving their products. Our proposed LSTMCNN model, trained on a dataset of 62,563 reviews, achieved impressive results with an accuracy of 95.84%, precision of 95.6% and recall of 95.8%. This significantly outperforms existing models, demonstrating the effectiveness of our approach. Moving forward, we aim to further enhance the accuracy of our system, track sentiment changes over time, and develop personalized product recommendations. These advancements will continue to increase the value and utility of online reviews in e-commerce.

Pengaruh Media Sosial, Celebrity Endorser, dan Online Customer Review Terhadap Keputusan Pemblian Produk Kosmetik Pinkflash di Kabupaten Dompu

This research aims to test and analyze the influence of Social Media, Celebrity Endorser, and Online Customer Reviews on the Purchasing Decisions of Pinkflash Cosmetic Products in Dompu Regency. The population in this study were people in Dompu Regency who had purchased Pinkflash cosmetic products. The sample in this study consisted of 94 respondents. The sampling technique employed in this research was non-probability sampling using purposive sampling method. Data analysis in this study utilized multiple linear regression analysis, and all data were processed using SPSS Statistics 25. The results of testing and statistical data analysis indicated that the indicators in the study were valid, where in the classic assumption test, the data were normally distributed and there was no multicollinearity. The results of this study indicate that Social Media and Online Customer Reviews have a positive and significant influence on Purchasing Decisions, while Celebrity Endorser does not have a significant influence on Purchasing Decisions.

Optimisation of Sentiment Analysis for E-Commerce

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.

Changing Foreign Customers Evaluations İn The Hotel Industry Amid The Covid 19 Pandemic: An Investigation On Online Customers Reviews

Customer satisfaction theories suggest that epidemics and pandemics might have an impact on customers' evaluation of hospitality services. The aim of this study is to investigate changing patterns of foreign customers in Sapanca hotels during the COVID-19 outbreak through online customers’ reviews. Sapanca region is a popular destination in tourism in terms of both location and hotel diversity. Customers’ evaluations were taken from TripAdvisor web site, findings were revealed by thematic analysis method and the MAXQDA 24 qualitative analysis program was used in the study. In order to analyze change, foreign customers’ evaluations in 2019 and in 2023 have been taken into account and to detect pre-pandemic and post-pandemic situation. According to findings, new themes have emerged after the pandemic phase and identified ten most discussed topics after pandemic. The study also found that main sources of dissatisfaction have changed. Findings of this study will help hotel managers understand the new trends that have changed after the pandemic.