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The process of oxidation is believed to be mainly responsible for different chronic diseases and the deterioration of food by reducing shelf life. Antioxidants are known to scavenge various free radicals like hydroxyl radicals, and peroxyl radicals, and also inhibit lipid peroxidation. An increasing number of peptides and hydrolysates possessing antioxidant capacity have been characterized by soybean and other soy-based products. Due to their high protein content, soybeans, and other soy products are considered to be a great source for generating antioxidative peptides using enzymatic hydrolysis or microbial fermentation. This paper focuses on the recent advances in soy-derived antioxidative peptides, especially the methods for producing the peptides, evaluating the antioxidant capacity in vitro and in vivo, and purifying and identifying the most active peptide fraction and the factors influencing their antioxidant activity. • Soybean and soy-based products hold significant potential as rich sources of antioxidative peptides. • This paper provides a comprehensive summary of bioactive peptides, with a particular focus on those derived from soybean. • The review explores the structure-activity relationship of soybean peptides, shedding light on their functional properties. • Further research is necessary to explore and optimize the utilization of these bioactive peptides for various applications. [ABSTRACT FROM AUTHOR]

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In recent years, the rapid advancement of technology has caused an increase in the development of wearable products. These are portable devices that can be worn by people. The main goal of these products is to improve the quality of life as they focus on the safety, assistance and entertainment of their users. The introduction of many new technologies has allowed these products to evolve into many different fields with multiple uses. The way in which the design of wearable products/devices is approached requires the study and recording of multiple factors so that the final device is functional and efficient for its user. The current research presents an in-depth overview of research studies dealing with the development, design and manufacturing of wearable products/devices and applications/systems in general. More specifically, in this review, a comprehensive classification of wearable products/devices in various sectors and applications was carried out, resulting in the creation of eight different categories. A total of 161 studies from the last 13 years were analyzed and commented on. The findings of this review show that the use of new technologies such as 3D scanning and 3D printing are essential tools for the development of wearable products. In addition, many studies observed the use of various sensors through which multiple signals and data could be recorded. Finally, through the eight categories that the research studies were divided into, two main conclusions emerged. The first conclusion is that 3D printing is a method that was used the most in research. The second conclusion is that most research directions concern the safety of users by using sensors and recording anthropometric dimensions. [ABSTRACT FROM AUTHOR]

As a fundamental task, review sentiment classification aims to predict a user's overall sentiment in a review about a product. Recent studies have proven critical effects of user and product attributes on this task. However, they usually adopt the same way to incorporate these two attributes, which does not fully consider their different effects and thus is not capable of leveraging them effectively. To address this issue, we propose a simple and effective review sentiment classification model based on hierarchical attention network, where different effects of user and product attributes are respectively captured via fusion modules and attention mechanisms. We further propose a training framework based on mutual distillation to fully capture the individual effects of user and product attributes. Specifically, two auxiliary models with only the user or product attribute are introduced to benefit our model. During the joint training process, our model and the auxiliary models boost each other via mutual knowledge distillation in an iterative manner. On the benchmark IMDB and Yelp datasets, our model significantly outperforms competitive baselines with a 1.6% improvement in average accuracy. When BERT embeddings are used as inputs, our model still performs much better than recent BERT-enhanced baselines. [ABSTRACT FROM AUTHOR]

Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors' markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models to detect and filter these fake reviews. However, with the same NLP techniques, machine-generated fake reviews are increasing exponentially. This work explores a powerful text representation technique called Embedding models to combat the proliferation of fake reviews in online marketplaces. Indeed, these embedding structures can capture much more information from the data compared to other standard text representations. To do this, we tested our hypothesis in two different Recurrent Neural Network (RNN) architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), using fake review data from Amazon and TripAdvisor. Our experimental results show that our best-proposed model can distinguish between real and fake reviews with 91.44% accuracy. Furthermore, our results corroborate with the state-of-the-art research in this area and demonstrate some improvements over other approaches. Therefore, proper text representation improves the accuracy of fake review detection. [ABSTRACT FROM AUTHOR]

Since September 2020, Fox News spawned an anti-critical race theory (CRT) disinformation campaign, that has reverberated in the whitestream's echo chamber. The disinformation largely appeals to white people who refuse to see racism, unless they feel it is impinging their rights. The campaign against CRT has penetrated the e-tailer site Amazon. com where books identified by Fox News as CRT texts have experienced increasingly hyperbolic and disinformed customer reviews. Encountering these reviews, we questioned how Amazon reviewers used a mundane platform to reify whiteness, while feigning hurt and ignorance. In this article, we present results from a qualitative critical race content analysis of Amazon.com customer reviews of four books identified by Fox News. A dialectical engagement between the tenets of CRT and key concepts of critical whiteness studies guided our analysis to describe how Amazon reviews enforced en/whitened postdigital spaces. Our results indicate that reviewers transmitted emo-social whiteness, discursively keeping white racism implacable yet, off the table. Framing our discussion, we examine current violent movements resulting from the anti-CRT echo chamber and its impact on education. [ABSTRACT FROM AUTHOR]

Oilseeds are grown mainly for their oil content but the residues (meals) that remain after defatting are excellent sources of plant protein ingredients. However, to serve as useful ingredients, the extracted proteins must meet industry expectations in terms of functional performance. Protein functionality is influenced by structural conformation, amino acid composition, type of polypeptides, presence of non‐protein materials (carbohydrates, lipids, and polyphenols), which in turn can be modified by the extraction method. Defatted oilseed meals are extracted mostly through the pH shift method, which involves alkaline solubilization followed by acid‐induced protein precipitation at the isoelectric point. A less popular method is called the protein micellar mass whereby the oilseed meal proteins are extracted with a NaCl solution, which is later diluted to reduce the ionic strength to a level where the proteins are no longer soluble and hence precipitate. A third method utilizes carbohydrases and phytases to first digest non‐protein materials from the oilseed meal into smaller units that are then removed by membrane ultrafiltration to leave behind a protein‐rich extract. These methods produce mainly two types of isolated oilseed proteins, concentrates (60%–89% protein content) and isolates (≥90% protein content), which can differ in terms of their protein conformation, solubility, and functionality as food ingredients. Therefore, this review provides an overview of the extraction and isolation as well as structural and functional properties of soybean, peanut, canola, hemp seed, sunflower, and sesame seed proteins. [ABSTRACT FROM AUTHOR]

Purpose: Recent literature identifies the importance of influencer-brand fit, a congruence between the narrative of the social media influencer (SMI) and the branded product being reviewed, on purchase intentions. In creating brand-related content, SMIs can post content that can be either sponsored by the brand or unsponsored. This research merges these literature streams to examine how influencer-brand fit impacts purchase decisions and whether sponsorship status moderates this relationship. Design/methodology/approach: Using a 2 (poor vs good influencer-brand fit) × 2 (sponsored vs unsponsored post) experimental design (n = 198), the relationship between influencer-brand fit and purchase intention, the mediating role of SMI trust and the moderating role of perceived sponsorship are tested. The PROCESS macro was used to analyze direct and indirect paths. Findings: The results demonstrate that influencer trust mediates the relationship between influencer-brand fit and purchase intention, highlighting the importance of a congruent influencer and brand image in both increasing influencer trust and purchase intentions. Surprisingly, despite the reductions in purchase intentions from conducting a poor-fitting review, purchase intentions are the same between a poor-fitting unsponsored review and a good fitting sponsored review. Practical implications: Decision-makers of both corporations and SMI personal brands should consider influencer-brand fit when selecting SMI partners to sponsor and brands to work with, respectively, and should aim for good fit between both parties. SMIs should avoid conducting sponsored, poor-fitting product reviews to limit reductions in trust. Influencers seeking to branch out of their area of expertise can initially consider unsponsored content before venturing into sponsored partnerships. Companies seeking to widen their reach through poor-fitting SMIs should consider alternative strategies to sponsorship. Originality/value: As sponsored content is common, it is necessary to merge the influencer-brand fit and influencer sponsorship literature. Additionally, this study considers the mediating role of influencer trust, an important variable in predicating purchase intentions as well as helping SMI grow their audience. [ABSTRACT FROM AUTHOR]

The current trend in the food industry is towards "clean label" products with high sensory and nutritional quality. However, the inclusion of nutrient-rich ingredients in recipes often leads to sensory deficiencies in baked goods. To meet these requirements, physically modified flours are receiving more and more attention from bakery product developers. There are various findings in the literature on high hydrostatic pressure (HHP) technology, which can be used to modify various matrices so that they can be used as ingredients in the baking industry. HHP treatments can change the functionality of starches and proteins due to cold gelatinization and protein unfolding. As a result, the resulting ingredients are more suitable for nutrient-rich bakery formulations. This review describes the information available in the literature on HHP treatment conditions for ingredients used in the production of bakery products and analyses the changes in the techno-functional properties of these matrices, in particular their ability to act as structuring agents. The impact of HHP-treated ingredients on the quality of dough and bakery products and the effects on some nutritional properties of the treated matrices have been also analysed. The findings presented in this paper could be of particular interest to the bakery industry as they could be very useful in promoting the industrial application of HHP technology. [ABSTRACT FROM AUTHOR]

This study evaluated the concentration of potentially toxic elements (PTEs) in milk and its products. The related studies regarding the concentration of PTEs in milk and its products were investigated in international databases such as Scopus, PubMed, and Web of Science. Finally, the health risk assessment was performed using Total Target Hazard Quotient (TTHQ) by the Monte-Carlo Simulation model. According to the results of 30 included articles among 981 retrieved studies, the ranking of metal concentration was Zn > Fe > Se > Cu > As > Ni > Cd > Pb and Zn > Se > Pb > Cu > As > Ni> Fe > Cd in milk and Butter, as well as Fe > Cu > Pb > Se > Zn > Ni> Cd> As and Fe> Zn> Se> Cu > Ni> > As> Cd> Pb in cheese and Yoghurt, respectively. Based on the type of continents, the highest concentrations of As (0.16 µg/kg), Cd (0.49 µg/kg), and Pb (0.49 µg/kg) were found in yoghurt, cheese, and butter, which was related to Western Pacific Region (WPRO) and European Region (EURO), respectively. Regarding trace elements, the highest concentrations of Fe (3.94 µg/kg), Zn (5.90 µg/kg), and Ni (0.23 µg/kg) were observed in yoghurt, butter, and yoghurt belonging to WPRO and Pan Americas Region (PAHO), respectively. In addition, Se (0.78 µg/kg), Cu (0.57 µg/kg), and Se (0.78 µg/kg) represented the highest concentrations in yoghurt and cheese and were associated with WPRO and PAHO, respectively. The non-carcinogenic risk assessment of the PTEs indicated a different risk pattern in various countries, and the TTHQ level in adult groups in all countrieswas lower than 1 except for Italy. Thus, the consumption of milk and its products is safe to the health of consumers. [ABSTRACT FROM AUTHOR]

Probiotic dairy products satisfy people's pursuit of health, and are widely favored because of their easy absorption, high nutritional value, and various health benefits. However, its effectiveness and safety are still controversial. This proposal aims to analyze the effect of probiotics on the quality characteristics of dairy products, clarify a series of physiological functions of probiotic dairy products and critically evaluate the effectiveness and safety of probiotic dairy products. Also, dairy products containing inactivated microorganisms were compared with probiotic products. The addition of probiotics enables dairy products to obtain unique quality characteristics, and probiotic dairy products have better health-promoting effects. This review will promote the further development of probiotic dairy products, provide directions for the research and development of probiotic-related products, and help guide the general public to choose and purchase probiotic fermentation products. [ABSTRACT FROM AUTHOR]

This paper empirically investigates how review moderation transparency affects the volume, length, and negativity of reviews. A change to the Yelp platform in 2010, introducing review moderation and displaying filtered reviews, created a natural experiment. We used a panel dataset of online reviews from the same set of restaurants on both the Yelp and TripAdvisor platforms in a difference-in-differences (DID) model to test how review moderation transparency affected our outcome variables. We found that increasing review moderation transparency negatively affects review volume but positively affects review negativity. The results also indicate that providing review moderation transparency reduces review length, especially for reviews with positive sentiment. Our findings suggest that providing review moderation transparency induces users to invest less effort in review contributions, especially when they are submitting positive reviews. We discuss the theoretical and practical implications of these results as they relate to the design and use of online review platforms. [ABSTRACT FROM AUTHOR]

The Mannich reaction is commonly used to introduce N atoms into compound molecules and is thus widely applied in drug synthesis. The Mannich reaction accounts for a certain proportion of structural modifications of natural products. The introduction of Mannich bases can significantly improve the activity, hydrophilicity, and medicinal properties of compounds; therefore, the Mannich reaction is widely used for the structural modification of natural products. In this paper, the application of the Mannich reaction to the structural modification of natural products is reviewed, providing a method for the structural modification of natural products. [ABSTRACT FROM AUTHOR]

Traditional science has long examined the causes of deforestation and forest degradation. Yet, it has often overlooked one of its main roots: corruption. As more recent research shows the intrinsic link between corruption and deforestation, this relationship deserves to be considered by the (legal) community in and of itself. For this reason, the present contribution analyses how the EU tackles the issues of corruption and deforestation through its legislative framework. It looks at the new Deforestation-Free Products Regulation (EU) 2023/1115, adopted on 31 May 2023. In particular, this paper identifies whether the new EU regime on deforestation effectively addresses corruption in the field. This includes a critical review of the Deforestation-Free Products Regulation, and more particularly, its context of adoption, its main features, its improvements, and its structural limitations. In addition, this new Regulation should not be cloistered fromthe broader legislative landscape. The new proposals fora Directiveon Combating Corruption and for a revision of the Environmental Crimes Directive have the potential to mitigate some of the identified limitations and to playarole in tackling the corruption rootsof deforestation. [ABSTRACT FROM AUTHOR]

Environmental product declarations (EPD) to EN 15804 provide information about the embodied carbon dioxide of construction products – their life cycle greenhouse gas emissions – alongside reporting the use of renewable and non-renewable primary energy and secondary fuels, among the other environmental indicators. As the number of EPD to EN 15804 increases, they become a useful data resource to consider these impacts. In moving towards a reduction in the embodied carbon of products, it is necessary to use renewable energy resources efficiently to allow the transition to net zero; this is because of the increasing demands on renewable energy to decarbonise industry, transport and domestic energy consumption and the limited capacity to expand renewable generation. This paper reviews published EPD data for structural and reinforcing steels, cement, bricks and structural timber products, and considers, for the cradle to gate 'product' life cycle stage, exploring the relationship of embodied carbon with embodied energy (total energy used), the balance of renewable and non-renewable energy, and the efficient use of energy. It is found, for bricks and timber, that EPD show products that use a greater percentage of renewable energy have higher embodied energy, suggesting a less efficient use of renewable energy for these products. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to identify methods for detecting composition and fraud in meat foods. Methods: An extensive literature review was conducted in 2022 using the electronic databases: Web of Science, Scopus, SID, and PubMed. The search was limited to articles published in English from 1970 to 2022. Search terms used were "fraud", "meat products", "Iran," "authentleication," "detection," and "adulteration". Results: Genetic-based molecular tests (PCR) and less use of histological and chemical tests were used to detect fraud and its type in meat products. PCR was used in 30 cases to identify the type of cheating in meat products such as sausages. Histological methods were used in 19 cases to detect type of violation. Conclusion: Molecular methods for detecting food fraud are highly accurate; therefore, they have the highest detection rate. [ABSTRACT FROM AUTHOR]

Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers' perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers' kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online reviews. Subsequently, the kansei data is adjusted based on consumers' kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers' kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method. [ABSTRACT FROM AUTHOR]

Olive oil, as well as by-products and waste that are left after production, particularly olive pomace and olive leaf, have been extensively researched as sources of phenolic compounds. These compounds are known for their biological properties and have been associated with the prevention of chronic non-communicable diseases. Metabolomics has been used as a methodological tool to elucidate the molecular mechanisms underlying these properties. The present review explores the health outcomes and changes in endogenous metabolite profiles induced by olive derivatives. A literature search was conducted using the scientific databases Scopus, Web of Science and PubMed, and the selected articles were published between the years 2012 and 2023. The reviewed studies have reported several health benefits of olive derivatives and their phenolic components, including appetite regulation, fewer cardiovascular disorders, and antiproliferative properties. This review also addressed the bioavailability of these compounds, their impact on the microbiota, and described biomarkers of their intake. Therefore, there should be further research using this methodology for a better understanding of the performance and therapeutic potential of olive derivatives. [ABSTRACT FROM AUTHOR]

We provide an extensive review of 17 independent and industry-funded studies targeting carbonyls in aerosol emissions of Heated Tobacco Products (HTPs), focusing on quality criteria based on the reproducibility of experiments, appropriate analytic methods, and puffing regimes. Most revised studies complied with these requirements, but some were unreproducible, while others failed to consider analytical variables that may have affected the results and/or produced unrealistic comparisons. We also provide a review of the literature on the physicochemical properties of heated tobacco and HTP aerosols, as well as the evaluation of HTPs by regulatory agencies, addressing various critiques of their relative safety profile. The outcomes from the revised studies and regulatory evaluations tend to agree with and converge to a general consensus that HTP aerosols expose users to significantly lower levels of toxicity than tobacco smoke. [ABSTRACT FROM AUTHOR]

Among the world, the most frequently discovered and fatal cancer in women is breast cancer (BC). From the perspective of public health, estimating the BC risk linked to dairy intake may aid in comprehensive management. In epidemiological research data on the association between eating dairy foods and the risk of BC are conflicting. Therefore, we sought to assess the link between dairy food consumption and the development of BC. To summarize and quantify the most recent findings on consuming milk or other dairy foods and the development of BC, we performed a systematic literature review. We checked through several databases for relevant publications published in English up to January 2022. Of the 82 articles identified, only 18 met the inclusion criteria and were analyzed. Nine Prospective, seven Retrospective and two Cross-Sectional studies were finally identified. Overall, dairy consumption was inversely associated with the risk of developing breast cancer. Future studies will help elucidate the role of dairy products in human health, and their use within a balanced diet should be considered. The effect of different types of dairy products, and possible dose-response relationships on BC risk remains unknown. Estimating BC risk associated with dairy consumption may help to take the decision-making of physicians and public health policy in developing preventive strategies to reduce its occurrence. This systematic review was conducted to assess dairy consumption and BC risk. Overall, inverse associations were found when looking at dairy consumption and BC risk. [ABSTRACT FROM AUTHOR]

Background: Last-mile delivery (LMD) is a challenging and costly supply chain process that involves direct customer interaction. In a recent study by the World Economic Forum, it was emphasized that last-mile logistics is an emerging research area. In particular, the European Commission Directorate General for Energy and Transport provides guidelines for abnormal road transport, such as heavy, bulky, and oversized (HBO) products. In the last few years, many research articles have been published on the topic of LMD. Several literature review studies from the perspective of different stakeholders have also been published. The delivery of HBO products is more challenging in many ways compared to parcel delivery. Although HBO products yield the most profitable sales, the LMD of HBOs is less studied compared to parcel deliveries. Methods: This research employed a descriptive review method to scan 195 published articles and reports related to LMD and HBOs. Results: Our results identified the challenges in the field and identified the need for separate approaches for different customer segments, such as rural and urban customers, as well as older and younger customers, when formulating delivery strategies. Conclusions: This paper highlights the research gap in this field by using a descriptive literature review methodology and presents academic contributions available so far, as well as challenges for further research. [ABSTRACT FROM AUTHOR]

The study's goal is to analyse and predict customer reviews of insurance products using various machine learning techniques. We gathered consumer rating data from the Yelp website and filtered the initial data set to only include insurance reviews. Following cleaning, the filtered summary texts were graded as positive, neutral or negative sentiments, and the AFINN and Valence Aware Dictionary for Sentiment Reasoning (VADER) sentiment algorithms were used to rate those sentiments. Furthermore, the current investigation employs five supervised machine learning approaches to divide customer ratings of insurance companies into three sentiment groups. The results of the current study revealed that the majority of customer reviews for the insurance products were negative, with the average number of words with negative sentiment being higher. In addition, current research discovered that while all of the approaches (decision tree, K Neighbours classifier, support vector machine (SVM), logistic regression and random forest classifier) can correctly classify review text into sentiment class, logistic regression outperforms in high accuracy. We analysed and predicted customer review messages using a variety of machine learning methods, which could help companies better understand how customers respond to their products and services. As a result, companies can learn how to use machine learning methods to better understand the behaviour of their customers. [ABSTRACT FROM AUTHOR]

This study aims to investigate the figurative language in most popular product review of makeup in Nikki Tutorials in Youtube channel. The data were the existing utterances delivered by the content creator. This research used the theory semantic of figurative language from Perrine (2018) to categorize the utterance that appeared in the data source into the function and reason of figurative language. A qualitative research design was employed in conducting this study followed by document analysis and observation as the instruments. This research used observational method that theorized by Sudaryanto (2015), to collect the data in the product review. The result showed that the kind of figurative language was separated into twelve types there are simile, metaphor, personification, apostrophe, metonymy, symbol, allegory, paradox, overstatement, understatement, synecdoche, and irony and the reason of figurative language specifically imaginative pleasure, bringing additional imagery, additional emotional intensity, and saying in brief compass. Related to the types, the researcher found 42 data that contains the kind of figurative language. There were 21 data of hyperbole, 4 data of simile, 1 data of metaphor, 7 data of personification, 2 data of apostrophe, 1 data of metonymy, 2 data of symbol, 2 data of paradox, 2 data of understatement. Whereas the reason of figurative language, the researchers found 30 data. 25 data increase emotional intensity, 2 data bringing additional imagery, 2 data saying in brief compass, 1 data afford imaginative pleasure. Moreover, the most used in the kind of figurative language is hyperbole whereas the reason of figurative language is increase emotional intensity which always appeared in the data analysis. [ABSTRACT FROM AUTHOR]

Salmonellosis is a disease transmitted by contaminated food and is one of the leading causes of infections worldwide, making the early detection of Salmonella of crucial importance for public health. However, current detection methods are laborious and time-consuming, thus impacting the entire food supply chain and leading to production losses and economic sanctions. To mitigate these issues, a number of different biosensors have been developed, including lateral flow assays (LFAs), which have emerged as valuable tools in pathogen detection due to their portability, ease of use, time efficiency, and cost effectiveness. The performance of LFAs has been considerably enhanced by the development of new nanomaterials over the years. In this review, we address the principles and formats of the assay and discuss future prospects and challenges with an emphasis on LFAs developed for the detection of different Salmonella serovars in food. [ABSTRACT FROM AUTHOR]

Recently, sentiment analysis has been a major business practice for an organization that helps analyze online reviews of different products. An organization can understand the customer's perception of their organizational products by analyzing reviews. On the other hand, this analysis process also helps to understand the customer's emotions about the organizational products. In this process, deep learning and distributed sensor networks play crucial roles in analyzing emotions. Five different steps of this analysis will provide accurate information about the customers' emotions on products. This analysis will help increase the product's value by understanding the customer's perception of where negative feedback improves the products. Through the help of this analysis, an organization will get several benefits that enrich its organizational image. On the other hand, this analysis process will face polarity issues, tone issues, comparative sentence analysis issues, and understanding idioms and emojis issues. The implementation of live API and proper sentiment analysis tools help to increase the effectiveness of this analysis. [ABSTRACT FROM AUTHOR]

With the increase in the availability of network connectivity, e-commerce has grown rapidly. User products and reviews have grown multi-folds for these online markets, leading to the web's data explosion. A recommender system overcomes this problem and provides personalised content to the user by mining a plethora of choices available on the web. In the group recommendations scenario, group members will have different tastes and achieving consensus is a nontrivial task. The common set of items taken from users' preferences is vital to provide recommendations. In practice, recommendations are generated by considering only common interest items, with the remaining items not playing a role in the recommendation. The items that are not of interest to a user based on her implicit or explicit feedback are called uninterested items. We hypothesise that these uninterested items may influence the overall recommendations, which can help us better interpret users' preferences. In this work, we also include the importance of uninterested items of the group members while generating the recommendation list. We have performed experiments over automatically identified groups and random groups to check the efficacy of the proposed models. We conducted experiments on publicly available real-world datasets. We found that incorporating uninterested item in group recommendations play a positive role in group recommendations while considering order and flexibility in user preferences. We observed that the overall group satisfaction scores are better in an automatically identified group. [ABSTRACT FROM AUTHOR]

Product reviews are written opinions submitted by consumers in assessing a product. The existence of product reviews is important because it can help consumers make better product purchasing decisions. But product reviews can also be unimportant if the quality of the information from the reviews is not helpful. This can be minimized if a classification is carried out to find out which reviews are helpful or not. For this to be achieved, this research will apply a support vector machine model using semantic and structural features to be able to classify review texts based on their characteristics. By applying the appropriate preprocessing stages, the final results show that the semantic features produce the highest F1-score value of 0.825. Whereas the structural features produce the highest F1-score value of 0.823. From this, it can be concluded that semantic features can be used to identify the characteristics of a review text that are helpful or not properly. This success also shows outstanding performance in classifying reviews as helpful or not compared to previous studies. [ABSTRACT FROM AUTHOR]

User-generated content (UGC) contains customer opinions which can be used to hear the voice of customers. This information can be useful in market surveillance, digital innovation, or brand improvisation. Automated text mining techniques are being used to understand these data. This study focuses on comparing two common text mining techniques namely: Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (pLSA) and evaluates the suitability of the methods in two differing marketing contexts: Reviews from a product category and from a single brand from Amazon. The objectives of review summarization are fundamentally different in these two scenarios. The first scenario can be considered as market surveillance where important aspects of the product category are to be monitored by a particular seller. The second scenario examines a single product, and it is used to monitor in-depth customer opinions of the product. The results support that depending on the objective, the suitability of the technique differs. Different techniques provide different levels of precision and understanding of the content. The power of machine learning methods, domain knowledge and Marketing objective need to come together to fully leverage the strength of this huge user-generated textual data for improving marketing performance. [ABSTRACT FROM AUTHOR]

Recent research has identified two aspects of the need to evaluate (NE) that are focused on interpersonal contexts: NE-expressing and NE-learning. Given that online word of mouth (WOM) is inherently interpersonal, we explore whether these two scales can predict consumers' likelihood of creating and seeking online WOM. We find that high NE-expressing does not always lead to a greater likelihood of sharing WOM. Although it does so for familiar products, for novel products, a basic level of knowledge must precede consumers' willingness to engage in WOM, such that consumers are most likely to write reviews when high on both scales. We also show that consumers with high NE-learning are more likely to seek recommendations in anonymous online forums. However, on identity-linked platforms, consumers higher in NE-expressing are more motivated to seek recommendations even when NE-learning is relatively low because identity-linkage makes even a request for information an opportunity for self-expression. These results have important implications for marketers who wish to understand the psychological drivers of online WOM. [ABSTRACT FROM AUTHOR]

Objective: A systematic review was conducted to evaluate the use patterns, health perceptions, and cardiopulmonary health effects of cigars. Data sources: PubMed and Google Scholar were searched for peer-reviewed articles published between June 2014 and February 2021. Search keywords included cigars, cigarillos, little cigars, and cardiopulmonary health outcomes. Study selection: Of 782 papers identified, we excluded non-English articles, review articles, commentaries, and those without empirical data on cigars. Three coders independently reviewed all articles and compared codes to resolve discrepancies. 93 articles met the inclusion criteria and were included. Data synthesis: Cigars have evolved from premium cigars to encompass little cigars and cigarillos (LCCs). LCCs are available in an array of flavors and at a price advantage, and as a result, are used by different groups compared to premium cigars. LCCs are more frequently used by youth, young adults, and those who identify as Black/African American. LCCs are often used in combination with other tobacco products, alcohol, and cannabis. Despite limited regulation, cigars generate smoke of a similar composition as cigarettes. Among the studies identified, evidence suggests that cigar use is associated with cardiovascular and pulmonary toxicity. Higher all-cause and cancer-related mortalities are associated with cigar use, particularly with more frequent and deeper inhalation, compared to non-tobacco users. Conclusions: LCCs are used more frequently by at-risk groups compared to premium cigars. Recent studies evaluating cigar cardiopulmonary health effects are limited but suggest cigars have similar health risks as conferred by cigarette smoking. With the use of LCCs and targeted marketing on the rise among high-risk groups, there is a critical need for continued research in this area. [ABSTRACT FROM AUTHOR]

Owing to the availability of high computing servers and clusters to process the data, factors such as data skewness, class imbalance, and scalability in big data cause slow processing performance. This study proposes a framework for load balancing in the Apache Spark cluster that makes efficient use of cluster resources and improves overall processing performance. The proposed method configures the Apache Spark cluster initially, to fix the optimal number of CPU cores and memory for each executor. The proposed scheme explores the trade-off between workload balance and communication efficiency, while for dynamic task allocation, coarse-grained and fine-grained data placement strategies are being used. A coarse-grained strategy handles the execution of datasets with larger partitions, but comparatively few executors, by transforming resilient distributed datasets (RDDs) into smaller datasets. Fine-grained strategy handles the execution of datasets with a large number of executors in comparison with partitions; the number of executors is considered equivalent to the number of knapsacks, and the resulting multidimensional knapsack problem is solved using particle swarm optimization to join the data partitions. The partitions were transformed into RDDs equivalent to the number of executors. All experiments were carried out using a large-scale dataset comprised of Amazon product reviews data in JSON format. The fine-grained and coarse-grained data placement strategies were found to be 26.12% and 42% faster in terms of execution time compared to the default data placement approach. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field. [ABSTRACT FROM AUTHOR]

Anemia remains a major public health problem, especially in low‐ and middle‐income countries. The World Health Organization recommends several interventions to prevent and manage anemia in vulnerable population groups, including young children, menstruating adolescent girls and women, and pregnant and postpartum women. Daily iron supplementation reduces the risk of anemia in infants, children, and pregnant women, and intermittent iron supplementation reduces anemia risk in menstruating girls and women. Micronutrient powders reduce the risk of anemia in children. Fortifying wheat flour with iron reduces the risk of anemia in the overall population, whereas the effect of fortifying maize flour and rice is still uncertain. Regarding non‐nutrition‐related interventions, malaria treatment and deworming have been reported to decrease anemia prevalence. Promising interventions to prevent anemia include vitamin A supplementation, multiple micronutrient supplementation for pregnant women, small‐quantity lipid‐based supplements, and fortification of salt with iodine and iron. Future research could address the efficacy and safety of different iron supplementation formulations, identify the most bioavailable form of iron for fortification, examine adherence to supplementation regimens and fortification standards, and investigate the effectiveness of integrating micronutrient, helminth, and malaria control programs. [ABSTRACT FROM AUTHOR]

Sunless tanning products have risen in popularity as the desire for a tanned appearance continues alongside growing concerns about the deleterious effects of ultraviolet radiation exposure from the sun. Dihydroxyacetone (DHA) is a simple carbohydrate found nearly universally in sunless tanning products that serves to impart color to the skin. The Food and Drug Administration (FDA), which regulates sunless tanning products as cosmetics, allows DHA for external use while maintaining that its ingestion, inhalation, or contact with mucosal surfaces should be avoided. Given its widespread use and a paucity of reviews on its safety, we aim to review the literature on the topical properties and safety profile of DHA. Available data indicate that DHA possesses only minimal to no observable photoprotective properties. In vitro studies suggest that, while DHA concentrations much higher than those in sunless tanning products are needed to induce significant cytotoxicity, even low millimolar, nonlethal concentrations can alter the function of keratinocytes, tracheobronchial cells, and other cell types on a cellular and molecular level. Instances of irritant and allergic contact dermatitis triggered by DHA exposures have also been reported. While no other side effects in humans have been observed, additional studies on the safety and toxicity of DHA in humans are warranted, with a focus on concentrations and frequencies of DHA exposure typically encountered by consumers. [ABSTRACT FROM AUTHOR]

Over the last decades, the increasing popularity of the Web came together with an extremely large volume of reviews on products and services useful for both companies and customers to adjust their behaviour with respect to the expressed opinions. Given this growth, Aspect-Based Sentiment Analysis (ABSA) has turned out to be an important tool required to understand people's preferences. However, despite the large volume of data, the lack of data annotations restricts the supervised ABSA analysis to only a limited number of domains. To tackle this problem a transfer learning strategy is implemented by extending the state-of-the-art LCR-Rot-hop++ model for ABSA with the methodology of Domain Adversarial Training (DAT). The output is a cross-domain deep learning structure, called DAT-LCR-Rot-hop++. The major advantage of DAT-LCR-Rot-hop++ is the fact that it does not require any labeled target domain data. The results are obtained for six different domain combinations with testing accuracies ranging from 35% up until 74%, showing both the limitations and benefits of this approach. Once DAT-LCR-Rot-hop++ is able to find the similarities between domains, it produces good results. However, if the domains are too distant, it is not capable of generating domain-invariant features. This result is amplified by our additional analysis to add the neutral aspects to the positive or negative class. The performance of DAT-LCR-Rot-hop++ is very dependent on the similarity between distributions of source and target domain and the presence of a dominant sentiment class in the training set. [ABSTRACT FROM AUTHOR]

The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories. Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the prompts and the augmented text. The predicted categories are the input to find similar phrases curated with extracting meaningful phrases (e.g., Nouns, Proper Nouns, NER (Named Entity Recognition) entities) to detect the aspect terms. The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment (MAMS) dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories. [ABSTRACT FROM AUTHOR]

Review platforms are viral online services where users share and read opinions about products (e.g., a smartphone) or experiences (e.g., a meal at a restaurant). Other users may be influenced by such opinions when deciding what to buy. The usability of review platforms is currently limited by the massive number of opinions on many products. Therefore, showing only the most helpful reviews for each product is in the best interest of both users and the platform (e.g., Amazon). The current state of the art is far from accurate in predicting how helpful a review is. First, most existing works lack compelling comparisons as many studies are conducted on datasets that are not publicly available. As a consequence, new studies are not always built on top of prior baselines. Second, most existing research focuses only on features derived from the review text, ignoring other fundamental aspects of the review platforms (e.g., the other reviews of a product, the order in which they were submitted). In this article, we first carefully review the most relevant works in the area published during the last 20 years. We then propose the User-Review-Item (URI) paradigm, a novel abstraction for modeling the problem that moves the focus of the feature engineering from the review to the platform level. We empirically validate the URI paradigm on a dataset of products from six Amazon categories with 270 trained models: on average, classifiers gain +4% in F1-score when considering the whole review platform context. In our experiments, we further emphasize some problems with the helpfulness prediction task: (1) the users’ writing style changes over time (i.e., concept drift), (2) past models do not generalize well across different review categories, and (3) past methods to generate the ground truth produced unreliable helpfulness scores, affecting the model evaluation phase. [ABSTRACT FROM AUTHOR]

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively. [ABSTRACT FROM AUTHOR]

The volume of data which is freely available on the Internet in the field of e-marketing is constantly increasing. Much of this data concerns consumers' perceptions and opinions of organizations' goods or services and as such is of interest to market intelligence collectors in the field of marketing, customer relationship management and customer retention. Sentiment analysis is used to analyze customer sentiment, marketing campaigns and product evaluations. It helps e-commerce companies better understand their customers and what they think about a product or service and how they feel about it. This information can be used to make decisions about future products and services, marketing campaigns or customer service issues. Using artificial intelligence techniques such as machine learning, natural language processing and sentiment analysis, it will be possible to create and implement systems that can analyze consumer opinions and feedback on e-commerce platforms. In this context, the objective of this paper is to compare supervised machine learning models based on their performance metrics in order to define the best performing model for consumer sentiment analysis, using a dataset that concerns a women's clothing e-commerce store and focuses on the comments written by customers on their different products. [ABSTRACT FROM AUTHOR]

The authors study how a consumer optimally allocates attention to favorable and unfavorable product-related information before making the purchase decision, when information processing is costly. They find that attention allocation depends on, among other factors, the consumer's prior belief about whether the product matches their needs and their unit information processing cost. A consumer processes both "confirmatory" and "disconfirmatory" information to their prior belief, but to different degrees under different conditions. In general, if the consumer has an extreme prior, or if the unit cost of processing information is high such that only a small amount of information is optimally processed, they process more confirmatory than disconfirmatory information; this offers a rational explanation for the phenomenon known as "confirmation bias." The authors also find that a seller can benefit by influencing the consumer's attention allocation by strategically choosing how much favorable and unfavorable information to make available for the consumer to process and by influencing the information processing cost, where the optimal strategy depends on the seller's ability to adjust product price. Surprisingly, a seller has a lower incentive to suppress unfavorable information when the consumer has a worse prior belief about product fit. The authors illustrate their model with an application to information provision in product reviews. [ABSTRACT FROM AUTHOR]

This research investigates reviewing experts on online review platforms. The main hypothesis is that greater expertise in generating reviews leads to greater restraint from extreme summary evaluations. The authors argue that greater experience generating reviews facilitates processing and elaboration and enhances the number of attributes implicitly considered in evaluations, which reduces the likelihood of assigning extreme summary ratings. This restraint-of-expertise hypothesis is tested across different review platforms (TripAdvisor, Qunar, and Yelp), shown for both assigned ratings and review text sentiment, and demonstrated both between (experts vs. novices) and within reviewers (expert vs. pre-expert). Two experiments replicate the main effect and provide support for the attribute-based explanation. Field studies demonstrate two major consequences of the restraint-of-expertise effect. (i) Reviewing experts (vs. novices), as a whole, have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) Experts systematically benefit and harm service providers with their ratings. For service providers that generally provide mediocre (excellent) experiences, reviewing experts assign significantly higher (lower) ratings than novices. This research provides important caveats to the existing marketing practice of service providers incentivizing reviewing experts and provides strategic implications for how platforms should adopt rating scales and aggregate ratings. [ABSTRACT FROM AUTHOR]