Online Product Reviews Research Articles

A Study on the Identification of the Water Army to Improve the Helpfulness of Online Product Reviews

Based on the perspective of identifying the water army, this paper uses the methods of machine learning and data visualization to analyze the helpfulness of online produce reviews, portray product portraits, and provide real and helpful product reviews. In order to identify and eliminate the water army, the Term Frequency-Inverse Document Frequency Model (TF-IDF) and Latent Semantic Index Model (LSI) are used. After eliminating the water army, three classification methods were selected to perform sentimental analysis, including logistics, SnowNLP, and Convolutional Neural Network for text(TextCNN). The TextCNN has the highest F1 score among the three classification methods. At the same time, the Latent Dirichlet Allocation mode (LDA) is used to extract the topics of various reviews. Finally, targeted countermeasures are proposed to manufacturers, consumers, and regulators.

Decision-making model for selecting products through online product reviews utilizing natural language processing techniques

Online product reviews are evaluations of products shared by customers on various online platforms, such as electronic commerce websites, social media, or dedicated review sites. These reviews offer valuable insights and opinions about products, potentially influencing the purchasing decisions of other prospective buyers. However, due to the vast volume of content provided by internet product evaluations, it can be challenging for new buyers to conduct a comprehensive qualitative assessment of competitive products. Utilizing natural language processing techniques, analyzing online product reviews posted on social media platforms can aid customers in making informed purchasing decisions. Hence, this article employs a 2-tuple linguistic q-rung orthopair fuzzy set for product selection based on online product reviews. In this study, we introduce a novel hybrid approach combining the Criteria Importance through Inter-Criteria Correlation (CRITIC) method with the Weighted Aggregated Sum Product Assessment (WASPAS) method. This hybrid method assists potential customers in evaluating alternative products by considering consumer opinions regarding product performance in the 2-tuple linguistic q-rung orthopair fuzzy environment. Moreover, for multi-attribute group decision-making problems, we develop a weighted average aggregation operator based on the 2-tuple linguistic q-rung orthopair fuzzy set. Finally, we apply the proposed approach to a real evaluation decision, validating its validity and practicality through parameter and comparison analyses.

Unveiling Users' Security and Privacy Concerns Regarding Smart Home IoT Products from Online Reviews

The Internet of Things (IoT) has revolutionized the global market with lifestyle products such as fitness trackers (FT), smart home speakers (SHS), and surveillance and security camera systems (SSCS). While offering convenience, these products also introduce potential security and privacy (S&P) risks to buyers, often going unnoticed. Consumers’ incomplete mental models of the risks involved and the information asymmetry between buyers and sellers only add to the problem. Understanding consumer concerns in online product reviews can play a crucial role in bridging the gap of such information asymmetry. By establishing a balanced flow of information between buyers and sellers, manufacturers can leverage genuine concerns expressed in reviews to enhance product features while educating users about misinformation in reviews. In this study, we collected FT, SHS, and SSCS product reviews from three Amazon domains: the US, the UK, and India. Using a keyword-based search method focused on S&P concerns, we discovered a considerable number of reviews expressing notable concerns regarding security and privacy. Our qualitative analysis revealed that data security is a common concern across all product types. Further, our quantitative analysis exposed significant geographic variations, with the concern ratio being higher in the US than in the UK for all device types and higher than in the Indian domain for security cameras. These findings highlight the need for tailored security measures and user awareness campaigns in different parts of the world to address the identified concerns effectively.

Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product Reviews

Recently the field of sentiment analysis has gained a lot of attraction in literature. The idea that a machine can dynamically spot the text’s sentiments is fascinating. In this paper, we propose a method to classify the textual sentiments in Twitter feeds. In particular, we focus on analyzing the tweets of products as either positive or negative. The proposed technique utilizes a deep learning schema to learn and predict the sentiment by extracting features directly from the text. Specifically, we use Convolutional Neural Networks with different convolutional layers. Further, we experiment with LSTMs and try an ensemble of multiple models to get the best results. We employ an n-gram-based word embeddings approach to get the machine-level word representations. Testing of the method is conducted on real-world datasets. We have discovered that the ensemble technique yields the best results after conducting experiments on a huge corpus of more than one million tweets. To be specific, we get an accuracy of 84.95%. The proposed method is also compared with several existing methods. An extensive numerical investigation has revealed the superiority of the proposed work in actual deployment scenarios.

Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulness

Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health & personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms.

Effects of product online reviews on product returns: a review and classification of the literature

AbstractProduct returns pose a significant challenge for online retailers, primarily due to consumer uncertainty, both before and after the purchase. These uncertainties may stem from factors such as the absence of a “touch and feel” experience, mismatched product expectations, or post‐purchase regret, where consumers change their minds. Online reviews, shared by previous consumers who have already experienced the products, can have a substantial impact not only on new consumers’ purchasing but also on return decisions. Surprisingly, there has been limited research to understand the influence of online reviews on product returns. This paper applies the input‐process‐output framework to conduct a comprehensive review and analysis of studies related to online reviews and product returns. Based on this review and analysis, a conceptual model is proposed, and an outline for the future research agenda is discussed.

Sentiment analysis of online reviews of energy-saving products based on transfer learning and LBBA model

With the exacerbation of global climate change and the growing environmental awareness among the general public, the concept of green consumption has gained significant attention across various sectors of society. As a representative example of green consumer products, energy-saving products play a crucial role in the timely realization of dual carbon goals. However, an analysis of online comments regarding energy-saving products reveals that the majority of these products still exhibit shortcomings in terms of efficacy, noise level, cost-effectiveness, and particularly, energy-saving appliances. This study focuses on the user-generated online comments data from the Taobao e-commerce platform for Grade 1 energy-saving refrigerators. By employing text mining techniques, the study aims to extract the essential information and sentiments expressed in the comments, in order to explore the consumption characteristics of Grade 1 energy-saving refrigerators. Moreover, the LBBA (LDA-Bert-BiLSTM-Attention) model is utilized to investigate the consumer topics of interest and emotional features. Initially, the LDA model is adopted to identify the attributes and weights of consumer concerns. Subsequently, the Bert model is pre-trained with the online comment data, and combined with the BiLSTM algorithm and Attention mechanism to predict sentiment categories. Finally, a transfer learning approach is utilized to determine the sentiment inclination of user-generated online comments and to identify the primary driving factors behind each sentiment category. This research employs sentiment analysis on online comments data regarding energy-saving products to uncover consumer sentiment attributes and emotional characteristics. It provides decision-makers with a comprehensive and systematic understanding of public consumption intentions, offering decision support for the efficient operation and management of the energy-saving product market.

Demystifying the Effect of Social Media Advertising on Purchase Intention of Millennials: Role of eWOM and Privacy Concerns

The substantial impact of businesses’ strong online presence on consumers’ spending habits, especially among decision-makers, cannot be overstated. One of the forms of online product reviews is electronic word-of-mouth (eWOM), and it has a significant impact on consumers’ attitude towards a product as well as their intentions to purchase. This article aims to provide a conceptual framework that assesses how eWOM and privacy issues affect Indian millennials’ attitude towards social media advertising and the subsequent effect of attitude on purchase intention. The data were gathered through non-probability sampling from 340 social network users utilising an online questionnaire through Google Forms. The adapted scales were validated using confirmatory factor analysis, followed by path analysis using SPSS AMOS 26.0 to examine numerous hypotheses that had been formulated. The results revealed significant relationships that are helpful in recognising the attitude and purchase intention of Indian millennials regarding social media advertisements. The study can be helpful to brand managers, marketing executives, and advertisers in generating social media commercials by incorporating key elements that can positively mould attitudes and further develop purchase intention.

AI-Estimated Online Product Review Based on the Comments of the Customer

Abstract: The primary objective of “AI-powered product review summarization” is to efficiently analyze large volumes of product reviews, estimating positive and negative sentiment specific to a product identified by its product name. Using the Text Blob library for sentiment analysis and NLTK for summary generation, the tool increases the ease of analysis and simplifies decisionmaking processes for sellers and consumers. Desired outcomes include time investment, an informed customer base, and ultimately a reduction in the purchase of defective products. By categorizing sentiment and generating concise summaries, the tool equips users with valuable insights, streamlines the review analysis process, and contributes to a more efficient and trustworthy e-commerce marketplace.

Evaluating The Impact of Online Product Review Credibility and Online Product Review Quality on Purchase Intention of Online Consumers

Evaluating The Impact of Online Product Review Credibility and Online Product Review Quality on Purchase Intention of Online Consumers

How review content, sentiment and helpfulness votes jointly affect trust of reviews and attitude

PurposeWe investigate the joint impacts of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of reviews and attitude toward the product/service reviewed.Design/methodology/approachWe performed three studies to test our research model, presenting participants with scenarios involving product reviews and prior users' helpful and unhelpful votes across experimental settings.FindingsA high helpfulness ratio boosts users’ trust and influences behaviors in both positive and negative reviews. This effect is more pronounced in attribute-based reviews than emotion-based ones. Unlike the ratio effect, helpfulness magnitude significantly impacts only negative attribute-based reviews.Research limitations/implicationsFuture research should investigate voting systems in various online contexts, such as Facebook post likes, Twitter microblog thumb-ups and up-votes for article comments on platforms like The New York Times.Practical implicationsOur findings have significant implications for voting system-providers implementing information techniques on third-party review platforms, participatory sites emphasizing user-generated content and online retailers prioritizing product awareness and reputation.Originality/valueThis study addresses an identified need; that is, the helpfulness votes as an additional trust cue and the joint effects of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of customers in reviews and their consequential attitude toward the product/service reviewed.

Harnessing Cloud Computing for Opinion Mining in Online Product Reviews

Harnessing Cloud Computing for Opinion Mining in Online Product Reviews

To Tell, Not to Yell: The Effect of Writer's Intents on Readers’ Perceived Helpfulness of Online Product Reviews

Perceived review helpfulness has been investigated extensively; however, the influence of the review writer's intents behind a review has not been considered. This study investigates the effect of the writer's intents to provide information, express emotions, or call for action on reviews’ helpfulness, thereby bridging the gap between the writer's perspective and the reader's perspective. The writer's intents are operationalized through speech acts, an implicit, yet systematic, approach in which the intents of the writer are captured at the linguistic level, that is, how things are said. A unique parallel approach based on both readers’ perceptions and linguistic theory is used to operationalize the speech acts. This approach allows multiple speech acts per sentence, and it can estimate the influence of each speech act separately. The results show that assertive acts positively impact review helpfulness, whereas expressive and call-for-action acts have a negative effect. This confirms previous literature showing that the major drivers of review helpfulness are often related to information delivery. In this research, the idea that consumers like reviews with accurate information is detected at the level of the writer's intent, which is new in the context of text analysis to assess online review helpfulness.

THE POWER OF ONLINE REVIEWS: ANALYSIS OF CUSTOMERS’ PERCEPTION AND PURCHASING DECISIONS IN SKIN CARE PRODUCTS

The research aimed to determine the customers' perception of online reviews, including its influence on their purchasing decisions for skin care products. Notably, the study seeks to determine the participants' sociodemographic profile about age, sex, marital status, level of education, and monthly income. It also seeks to determine how the participants think about the rating, credibility, and valence of online reviews of skin care products, as well as the impact of these reviews on the participants' decisions to buy skin care products. The study used descriptive research methods to describe the variables and to obtain information to describe the study. It analyzes the perception and purchasing decisions in online reviews for skincare products. The study's participants were chosen using convenience sampling, and data was gathered from 150 individuals. Most of the participants are 18-25 years old and females. Most of them are single and college undergraduates with 10,000 below earning monthly. The study also revealed that most participants had very high positive perceptions of online reviews of skincare products in terms of rating, credibility, and valence. Furthermore, the study indicates that online reviews highly influence online shoppers purchasing decisions for skin care products.

Applications and Ethical Challenges of Artificial Intelligence Technologies in E-Commerce

Abstract The theory and technology of artificial intelligence are becoming more and more mature, and its application fields are expanding, greatly changing people’s lifestyles and work modes. The article sorted out the power mechanism and interaction relationship of AI technology to promote the development of e-commerce, and innovated the development mode of e-commerce based on AI empowerment, but also illustrated the ethical challenges brought by its application. Combining the BERT model and LSTM model to establish a sentiment analysis model for online reviews of e-commerce products, using the BiGRU model to obtain users’ long-term preferences, and learning users’ short-term preferences through the multi-head self-attention mechanism to establish a personalized recommendation model for e-commerce products. The study explores the algorithmic discrimination of AI-enabled e-commerce and introduces economic concepts to analyze the consumer’s perceived betrayal and kill-off phenomenon through experiments. At the lowest level, the individualized recommendation model only has an MAE value of 0.679, while the BERT-LSTM model has a sentiment analysis accuracy of 0.837. The average level of perceived betrayal and deliberate attribution of the low-priced group is 4.035 and 4.369, respectively, which is lower than that of the high-price group is lower, and the killing phenomenon brought by algorithmic price discrimination can be alleviated when the elasticity of demand is 0.4.AI-enabled e-commerce needs to be legally regulated from the five perspectives of legislation, law enforcement, judiciary, law-abiding, and law-monitoring as a way of circumventing the algorithmic discrimination and ethical challenges brought about by AI technology.

Contextual Meaning-Based Approach to Fine-Grained Online Product Review Analysis for Product Design

Contextual Meaning-Based Approach to Fine-Grained Online Product Review Analysis for Product Design

The Effects of Featuring Product Sampling Reviews on E-Tailer Websites

Given the significant influence of online product reviews on consumer purchases, firms are trying to be more proactive in leveraging the influence of reviews. One way of doing this is to offer free product samples to consumers to incentivize product reviews, displaying them along with a disclosure in the existing pool of organic reviews in the hopes of stimulating sales. However, it is not clear whether such product sampling reviews are indeed beneficial to sales, considering that they may have a nontrivial influence on the generation of organic reviews. Based on data from a leading e-commerce platform in China, we found that sponsored product sampling reviews promoted product sales but also reduced the volume of organic reviews for the targeted product. However, the net effect on sales remained positive, suggesting that e-tailers may wish to utilize sponsored product sampling reviews. We also conducted a scenario-based experiment to reveal how the nature of product sampling reviews affects consumers’ brand quality and fairness perceptions. Based on our findings we offer future research directions to improve the understanding of how to better harness the power of sponsored reviews.

The Impact of Media Publicity and Online Product Reviews on Consumers’ Purchase Intentions of Energy-Efficient Appliances

In the changing global economy, energy security has become a central concern for the sustainable development of nations. The limited energy supply in developing countries has led governments and enterprises to explore efficient energy resources. The consumption of energy-efficient appliances is a crucial way to address energy scarcity. This study aims to predict consumer purchase intention for energy-efficient household appliances. The study extends the Stimulus – Organism – Response model by incorporating green perceived value and customer trust as mediators between external stimuli and consumers’ behavioral responses. Data was collected from 287 household heads in Vietnam and analyzed by applying partial least squares structural equation modeling. Results show that media publicity, online product reviews, green perceived value, and customer trust all significantly correlate with consumers’ purchase intention. Results further indicate that green perceived value mediates the relationship between media publicity, online product reviews and consumers’ purchase intention. Moreover, customer trust in energy-efficiency labels is a significant mediator between media publicity, online product reviews and consumers’ purchase intention. The research offers theoretical and practical insights into green consumption and the promotion of energy efficiency in emerging markets.

Dynamic relationship changes between reviewers and consumers in online product reviews

This research examines online product reviewers and their influence on consumers who read and endorse those posted reviews. We examine three primary research questions. First, what kinds of products will reviewers review, and how do these choices influence consumers’ endorsements (i.e., user votes) of the reviews? Second, how does the volume of reviews that a reviewer writes affect consumers’ perceptions of how helpful the reviews are? Third, if reviewers are more positive (or critical), does that influence consumers’ endorsements? This research examines not only the static effects of these multifaceted relationships but also the dynamic changes in the effect magnitudes over time. Using the time-varying effect model (TVEM) with 11 years of Yelp review data, we find that a reviewer's influence on consumers tends to increase over time as the reviewer accumulates more experience in their review writing activities. However, we find two exceptions in the monotonically increasing pattern with Product Satisfaction and Reviewer Rating Positivity, demonstrating U-shaped and inverted ∩-shaped impacts over time, respectively. We explain such results with the “expertise trap” phenomenon. Thus, our research offers new insights to platform managers and product managers in governing influential reviewers.

The Influence of Argument Quality and Information Richness on Trust in Halal Cosmetic Reviews

In the digital age, halal cosmetic products are becoming increasingly popular. Online reviews play an important role in consumers' purchasing decisions, as they cannot see and check the quality of the products directly, but only through the screens of their gadgets. Under Islamic law, the consumption of halal cosmetics is not only obligatory but also a lifestyle choice. The purpose of this study was to examine the effect of argument quality and information richness on perceptions of information quality and trustworthiness of halal product reviews on the Shopee platform. We utilized a survey design involving 120 participants and applied ANOVA with a regression approach for analysis. Our results show that argument quality and information richness have a positive impact on perceived information quality. Furthermore, perceived information quality positively affects trust in halal product reviews. The results highlight the importance of providing high quality arguments and information in online reviews to increase consumer trust in halal cosmetic products. Furthermore, this study highlights the role of the Shopee platform in shaping consumer perceptions of online reviews, especially in the context of halal cosmetics. Marketers can use these findings to enhance their brand credibility and increase consumer trust through high-quality online product reviews. This study is useful for muslim marketers so that they can increase sales by motivating consumers to provide quality reviews that can be adopted by other consumers.