Product Reviews Research Articles

A comprehensive review of fresh water production from atmospheric air – techniques, challenges and opportunities

A comprehensive review of fresh water production from atmospheric air – techniques, challenges and opportunities

Weakly Supervised Deep Embedding for Product Review Sentiment Analysis

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this project, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines. In our project we have used algorithms like Decision Tree (DT) and Multi Layer Perceptron (MLP). All are measured in terms of accuracy

Microplastics from petroleum-based plastics and their effects: A systematic literature review and science mapping of global bioplastics production.

The use of bioplastics is a new strategy for reducing microplastic (MP) waste caused by petroleum-based plastics. This problem has received increased attention worldwide, leading to the development of large-scale bioplastic plants. The large amount of MPs in aquatic and terrestrial environments and the atmosphere has raised global concern. This article delves into the profound environmental impact of the increasing use of petroleum-based plastics, which contribute significantly to plastic waste and, as a consequence, to the increase in MPs. We conducted a comprehensive analysis to identify countries that are at the forefront of efforts to produce bioplastics to reduce MP pollution. In this article, we explain the development, degradation processes, and research trends of bioplastics derived from biological materials such as starch, chitin, chitosan, and polylactic acid (PLA). The findings pinpoint the top 10 countries demonstrating a strong commitment to reducing MP pollution through bioplastics. These nations included the United States, China, Spain, Canada, Italy, India, the United Kingdom, Malaysia, Belgium, and the Netherlands. This study underscores the technical and economic obstacles to large-scale bioplastic production. Integr Environ Assess Manag 2024;20:1892-1911. © 2024 SETAC.

A Short Review of Second-Generation Isobutanol Production by SHF and SSF

As isobutanol exhibits higher energy density and lower hygroscopicity than ethanol, it is considered a better candidate biofuel. The sustainable supply of inedible biomass and lack of competition with the food supply have stimulated significant worldwide interest in the production of isobutanol from this resource. Both separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) have been applied to isobutanol production to effectively utilize inedible biomass as a feedstock. However, both processes have various challenges, including low isobutanol yield and high production costs. This review summarizes the potential of isobutanol as a biofuel, methods for conferring isobutanol productivity, recent experimental studies, and developments in both SHF and SSF with the isobutanol-producing strains. Challenges to increasing the isobutanol yield and various suggestions for improvements to enable commercial production are also discussed.

Unfolding the consequences of customer satisfaction: Information from social media as a moderator

The present study examines the relationship between customer satisfaction and eWOM, value co-creation, and repurchase intentions. It also tested the moderating effect of information from social media. A conceptual model was developed, and hypotheses were tested using hierarchical regression. The sample of participants in the present study was limited to those who had extensive experience with online shopping, read product or service reviews frequently, and shared them online. We have received data from 455 respondents. The study's empirical findings expose that customer satisfaction is positively and significantly related to eWOM, value co-creation, and repurchase intention. The study also revealed that social media information moderates the relationship between customer satisfaction, value co-creation, and repurchase intention.

Aspect Based Sentiment Analysis using Deep Learning Algorithm: A Review

Compared to broad sentiment analysis, aspect-based sentiment analysis (ABSA), which aims to predict the sentiment polarities of the designated features or entities in text, can produce more precise results. Product reviews and social media comments are examples of texts that can be used to identify and analyze sentiments. Another type of text analysis is known as sentiment analysis subtask, or ABSA. Deep learning methods have demonstrated efficacy in managing the intricacy of natural language and obtaining subtle emotions linked to many facets of a good or service. Deep learning has become increasingly popular in many applications, and in recent years, both the academic and industry communities have given ABSA a great deal of attention. Overall, ABSA’s deep learning efforts have been successful in advancing sentiment analysis skills, which has given rise to important insights into how consumers view and respond to various features of goods and services. Deep learning-based methods are probably going to have a big impact on how sentiment analysis apps develop in the future as technology keeps developing. The size of the dataset, the task’s difficulty, and the computer resources available all play a role in the deep learning method selection. To attain optimal performance, many state-of-the-art ABSA models pretrain on huge corpora and then fine-tune on task-specific datasets.

Clinical and economic evaluation of tralokinumab for atopic dermatitis.

Tralokinumab is the first selective interleukin 13 inhibitor approved for moderate to severe atopic dermatitis. This article reports the findings of a comprehensive literature review and extensive economic analysis to assess tralokinumab's safety, effectiveness, and cost. Evidence synthesis involved evaluating comparative effectiveness and conducting economic sensitivity analyses. This review was prepared by the University of Connecticut School of Pharmacy Academy of Managed Care Pharmacy (AMCP) Student Chapter. The student author group won the AMCP National Pharmacy and Therapeutics competition for their tralokinumab product review in March 2023.

Evaluating the impact of periodic product review on pharmaceutical production process improvement: A case study

The aim of this study was to evaluate the impact of Periodic Product Review (PPR) on improvements in the production process of pharmaceuticals by statistically analyzing the in-process control of the parameters average weight by coating and hardness. Methods. For this purpose, a case study was conducted in a pharmaceutical industry analyzing the statistical PPR data of a coated tablet from 2021 and 2022. Diagrams and tables based on probability and statistical theory were used for the analysis. Results. The data analysis revealed that PPR was effective in helping to identify problems in the production process, which were then resolved. As a result, the production performance of this drug product was significantly improved, as evidenced by the reduction in the variability of analytical results and process performance indicators within the acceptable range. Conclusion. The results of this work show that PPR is an important tool for ensuring the quality of pharmaceutical products. Statistical analysis of PPR data makes it possible to identify trends and patterns that may indicate the need for improvements in the production process.

Pemanfaatan Key Opinion Leader (KOL) Brand Scarlett Whitening dalam Pemasaran Digital Pada Generasi Z

In facing the rapidly developing digital era, Scarlett Whitening has succeeded in combining traditional business principles with digital marketing methods. One important approach taken is through collaboration with Key Opinion Leaders (KOL) on various social media platforms, such as Instagram and TikTok. With appropriate KOL support, Scarlett Whitening succeeded in reaching generation Z, its main target market, with a more personal approach and in line with brand needs. This research aims to analyze the use of KOL brand Scarlett whitening in Generation Z. The research was conducted using a descriptive qualitative approach. Research data comes from search results in various digital databases and literature reviews. The research results show that by utilizing Key Opinion Leaders (KOL) in digital marketing strategies, Scarlett Whitening has succeeded in aligning the use of KOLs in its digital marketing strategy, such as through collaboration with KOLs who have significant influence and reach among the younger generation. The selected KOLs are not only popular because of their large number of followers, but also the content they present is of high quality and suits the interests and needs of the target market. Scarlett Whitening's main target market is teenagers aged 15-34 years, namely Generation Z, who have a tendency to be very familiar with digital technology. With a more personal approach and in line with the needs of the target market, Scarlett Whitening has succeeded in reaching generation Z with quality content that suits the interests and needs of the target market. The content produced by KOL for Scarlett Whitening often contains product reviews, usage tutorials, and testimonials from consumers. Through this content, information about the quality, safety and effectiveness of the Scarlett Whitening brand can be conveyed in an authentic and convincing way to potential consumers.

리뷰 데이터 감성 분류 성능 향상을 위한 Fine-tuning 방법

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews

<p class="MsoNormal" align="justify"><span style="font-family: Times New Roman;">This study explores personalized recommendation strategies within Amazon's product review system using the Logistic Regression algorithm. By analyzing user behavior and review data, a predictive model was developed to forecast user preferences for specific products. The research employed extensive real-world data and validated the model's effectiveness and accuracy through empirical analysis. Findings indicate that the proposed personalized recommendation system significantly enhances user experience and increases product sales. The study contributes an effective recommendation algorithm for e-commerce platforms, offering practical implications for enhancing user engagement and optimizing marketing strategies in competitive markets.</span></p>

Brief Reviews of Books and Products: NASA TOPS Open Science 101

A brief review of NASA TOPS Open Science 101 version 1.0 (2023). Zenodo. https://doi.org/10.5281/zenodo.10161527. Free of charge.

Brief Reviews of Books and Products: Copyright: Best Practices for Academic Libraries

A brief review of editors Donna L. Ferullo and Dwayne K. Buttler book, Copyright: Best Practices for Academic Libraries (2023). Rowman and Littlefield. 282 pages, cloth/hardcover and ebook. ISBN: cloth/hardcover (9781538168219); ISBN: ebook (9781538168226). $65.00 on Amazon.

Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM

In this work, we evaluated the efficacy of Google’s Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon’s fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks.

빅데이터 분석을 활용한 합기도의 인식변화 연구

Objectives This study intends to collect words related to hapkido from domestic portal sites and compare them around the last 10 years and after to provide basic data for future hapkido-related research and seek development plans. Methods Using ‘Hapkido’ as a keyword, data from 10 years ago(2013. 1. 1 - 2013. 12. 31.) and 10 years later(2023. 1. 1. - 2023. 12. 31.) were collected by Naver, Daum, and Google, and they were compared after network analysis, CONCOR analysis, and emotion analysis. Results As a result of network analysis, it was confirmed that Taekwondo, Dan, exercise, and gym were the most frequent 10 years ago, followed by Taekwondo, exercise, gym, and competition 10 years later. In the frequency of simultaneous appearance, Taekwondo-dan, Taekwondo-judo, Judo-dan, etc. were high 10 years ago, and Taekwondo, dan, Judo, and additional points were high 10 years later. According to the CONCOR analysis, 10 years ago, license, judo, Dan, police, additional points, Taekwondo, etc. were gathered in Group 1, Master, Recommendation, Exercise, Gym, Self-defense skills were gathered in Group 2, Competition, Daily Sports, Participation, Athletes, and Hosting were gathered in Group 3, and Delivery Status and Product Review were gathered in Group 4. Ten years later, Boxing, Taekwondo, Police, Exercise, Jiu-Jitsu, Dan, Kickboxing were gathered in Group 1, and Evaluation, Jump rope, Self-defense, and Gym were gathered in Group 2, Athletes, The whole country, and Competitions were gathered in Group 3, and parents were gathered in Group 4. As a result of emotional analysis, positive words accounted for a total of 4,398 (69.38%) and negative words accounted for a total of 1,941 (30.62%) 10 years ago, and positive words accounted for a total of 5,261 (73.54%) and negative words 1,893 (26.46%) 10 years later. Conclusions First, Hapkido should develop its own skills and programs, expand the benefits of Hapkido licenses, and establish a strict and objective promotion review process. And it is necessary to actively seek ways to receive the Hapkido seal by promoting the advantages of evenly developing the mind and body. In addition, the development should be accelerated through the large scale of Hapkido organizations. Second, Hapkido Gymnasium should develop and apply various Hapkido training programs to increase the number of trainees. Third, it is necessary to apply training programs tailored to the tendencies and characteristics of trainees, and to create a safe training environment where comfortable and free training is possible.

Sentiment analysis in product reviews in Thai language

Sentiment analysis in product reviews in Thai language

Objective and neutral summarization of customer reviews

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

Sentiment Analysis of Amazon Reviews using NLTK Vader and Robert

With the advancement of technology, huge volume of data is exchanged every-day, example - from social media sites like Instagram, Twitter, or e-commerce sites like Amazon, Flipkart etc. The human brain is unable to process this large amount of data (mainly out of which is unstructured) and this results in the development of technology which do the work of humans. One such methodology is Sentiment Analysis. Sentiment analysis plays a crucial role in understanding public opinion and consumer behavior. In this paper, we conduct a comparative study of sentiment analysis on Amazon product reviews using two different sentiment analysis approaches: VADER (Valence Aware Dictionary and Sentiment Reasoner) and RoBERTa (Robustly Optimized BERT Approach). We explore the effectiveness of these models in capturing the sentiment expressed in Amazon reviews, considering various aspects such as accuracy, computational efficiency, and generalization capabilities. Our findings provide insights into the strengths and limitations of each approach, contributing to the advancement of sentiment analysis methodologies. Keywords Sentiment Analysis, Roberta, Vader, Machine Learning.

Overview of Symbolic Violence: A Review of the Literature

Objective: This study aims to investigate the literature on symbolic violence over the past decade, with the purpose of understanding its nature and manifestations in contemporary society. Theoretical Framework: Within the context of gender studies and the increasing political impact of feminism, symbolic violence is defined as the subjugation of individuals to others through processes of socialization that naturalize power relations. These relations, based on asymmetries such as gender, become unquestionable, providing a solid foundation for research on symbolic violence. Method: A systematic review of scientific productivity on symbolic violence was conducted by tracing databases such as Scopus and Web of Science. Research articles published between 2013 and 2023 in English and Spanish, and with full access, were considered. Results and Discussion: It was found that both material and symbolic structures serve as bases for symbolic violence against women, legitimizing within contemporary patriarchal society. These findings are discussed in relation to the theoretical framework, highlighting identified implications and relationships, as well as potential discrepancies and limitations of the study. Research Implications: Practical and theoretical implications of the study are discussed, providing insights into how the results can influence practices in the field of gender studies and the fight against gender-based violence. These implications could encompass public policies, educational programs, and community actions. Originality/Value: This study contributes to the literature by highlighting the persistence and complexity of symbolic violence in contemporary society. The relevance and value of this research are evidenced by its ability to inform and guide interventions aimed at combating gender-based violence and promoting equity and social justice.

Reputation System as a Digital Socio-Economic Institution: Willingness to Sell in Online Markets

This study examines the features and big data consisting of digital trace records from economic actions within Bukalapak's reputation system and their impact on the willingness to sell (WTS) electronic products. Digital mixed content analysis was used to analyze the rapidly evolving digital-social-based economic institution infrastructure. Data were collected, analyzed, and visualized using Python and analyzed using the concepts of new economic institutions and WTS. The findings indicate that the reputation system is a formal element comprising algorithms, rules, scripts, and patterns. This system facilitates social processes through feedback mechanisms in product descriptions, comments, reviews, ratings, and badges, which generate informal elements such as norms, trust, and symbolic values. These two elements are interconnected into an institutional framework whose function is to ensure that the market, characterized by high levels of anonymous transactions, uncertainty, and asymmetric information, can remain stable and meet the expectations of the actors involved.