User Reviews Research Articles

Are Your Comments Positive? A Self-Distillation Contrastive Learning Method for Analyzing Online Public Opinion

With the popularity of social media, online opinion analysis is becoming more and more widely and deeply used in management studies. Automatically recognizing the sentiment of user reviews is a crucial tool for opinion analysis research. However, previous studies mainly have focused on specific scenarios or algorithms that cannot be directly applied to real-world opinion analysis. To address this issue, we collect a new dataset of user reviews from multiple real-world scenarios such as e-retail, e-commerce, movie reviews, and social media. Due to the heterogeneity and complexity of this multi-scenario review data, we propose a self-distillation contrastive learning method. Specifically, we utilize two EMA (exponential moving average) models to generate soft labels as additional supervision. Additionally, we introduce the prototypical supervised contrastive learning module to reduce the variability of data in different scenarios by pulling in representations of the same class. Our method has proven to be extremely competitive, outperforming other advanced methods. Specifically, our method achieves an 87.44% F1 score, exceeding the performance of current advanced methods by 1.07%. Experimental results, including examples and visualization analysis, further demonstrate the superiority of our method.

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>

The Voice of the Guests: Analysing Airbnb Reviews as a Representative Source for Tourism Studies

User-generated content on social media has led to a new form of communication known as electronic word of mouth, which generates millions of comments about goods and services on the internet every day. This openly accessible content is crucial for prospective consumers as it helps in decision-making, but it is also valuable for product or service providers, as it allows them to improve their businesses based on user reviews, some of which are highly detailed. Another interest group that benefits from these comments are researchers and academics, as it allows them to obtain and analyse information for their studies at a relatively low cost in terms of time and money. The present study aims to perform a sentiment analysis of comments posted by guests staying at a property offered by Airbnb to determine whether their opinions about their experience are positive or negative. However, before doing so, it is necessary to find out the percentage of people who write a review about the service received on Airbnb to verify the representativeness of the reviews on this platform. To achieve this, thousands of comments posted in one year on Airbnb for the four most touristic cities in Spain are analysed: Madrid, Barcelona, Seville, and Valencia. The results show that opinions on Airbnb are much more representative compared to other platforms, as a very high participation rate is calculated. Furthermore, these opinions are predominantly positive, indicating a high level of satisfaction with the service provided.

Analisis Persepsi Pengguna Aplikasi Lalamove Dengan Menggunakan Metode Technology Acceptance Model (TAM)

A number of on-demand platforms have arisen with the goal of making the delivery of products easier for all members of society, and Lalamove is one of them. Technological improvements have been a big driver in the growth of the logistics business. The study's overarching goal is to investigate users' impressions and thoughts about the Lalamove app via an exploratory lens. To have a better grasp of the topic at hand, this descriptive qualitative study used a content analysis strategy, which entails reducing the amount of data collected. This study's sample was chosen using a non-probability sampling approach, with a focus on user reviews of the Lalamove app located on the Google Play Store marketplace. Based on the TAM, which centres on the two primary components of perceived utility and perceived ease of use, this model attempts to explain why people adopt new technologies. Based on these findings, it seems that the app is still not as user-friendly as it might be. Despite the app's usefulness in enhancing delivery effectiveness, customers still face hurdles such as connecting couriers with users, registration issues, order cancellation, a tracking system that isn't up to par, and payment options that don't fulfil their expectations. Because of this, the Lalamove app's general usefulness and convenience are diminished due to persistent issues.

Integrating Heuristic Evaluation and Think-Aloud Protocols By Applying Nielsen’s Metrics on Indonesian e-Pangan Application

Usability testing has emerged as one of the most important aspects of software testing to provide high-quality software to users. The purpose of this study is to evaluate the usability of an Indonesian mobile government application known as e-pangan. Because of the growing number of mobile phone users, the government has deployed mobile applications to make it easier for people to obtain information. Unfortunately, mobile government is not user-friendly and still has a lot of issues, as evidenced by user reviews in the Google Play store. The study employed a hybrid usability methodology that included heuristic evaluation and think-aloud protocols. The mobile government application Panel Harga Pangan will be tested to represent the study of this testing. Furthermore, the heuristic evaluation is performed by a usability expert, while the think-aloud protocols are performed by ten users. Nielsen's metrics are applied to measure the learnability, efficiency, memorability, error rate, and satisfaction of the application. The results of the research show that the application has an average score of 66%, indicating that while some users might discover it challenging others might discover it to be straightforward. This research is expected to be beneficial for usability practitioners or developers to develop a user-friendly e-government application.

Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed language

Social media, e-commerce, and other online platforms have witnessed tremendous growth in multilingual users. This requires addressing the code-mixing phenomenon, i.e. mixing of more than one language for providing a rich native user experience. User reviews and comments may benefit service providers in terms of customer management. Aspect based Sentiment Analysis (ABSA) provides a fine-grained analysis of these reviews by identifying the aspects mentioned and classifies the polarities (i.e., positive, negative, neutral, and conflict). The research in this direction has mainly focused on resource-rich monolingual languages like English, which does not suffice for analyzing multilingual code-mixed reviews. In this paper, we introduce a new task to facilitate the research on code-mixed ABSA. We offer a benchmark setup by creating a code-mixed Hinglish (i.e., mixing of Hindi and English) dataset for ABSA, which is annotated with aspect terms and their sentiment values. To demonstrate the effective usage of the dataset, we develop several deep learning based models for aspect term extraction and sentiment analysis, and establish them as the baselines for further research in this direction. 11Codes and the complete dataset have been made available on https://www.iitp.ac.in/~ai-nlp-ml/resources.html and at Github repository: https://github.com/20118/ABSA-MIX.

Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors

The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user reviews of the application have emerged. Therefore, sentiment analysis is needed to provide a deeper understanding of user perceptions and to classify user reviews of the Digital Population Identity Application. Sentiment analysis is a computational study of opinions, feelings, and emotions expressed in text, using the K-Nearest Neighbors method, which is a classification method based on the closest distance or similarity to objects in the training data. Using 5000 relevant review data from September 2022 to December 2023, after labeling them into positive, negative, and neutral sentiment classes, the results show 3581 negative sentiments, 1031 positive sentiments, and 388 neutral sentiments. Testing was conducted by applying the K-Nearest Neighbors method in the classification stage, testing this method by varying K values from 1 to 10. The best results were obtained with a training data ratio of 90% to testing data ratio of 10%. The best results were achieved at K values of 8, 9, and 10, with an accuracy of 81%, precision of 82%, recall of 95%, and an F1-Score of 88%. With a training data ratio of 70% to testing data ratio of 30%, the best results were obtained at K values of 6, 7, 8, 9, and 10, with an accuracy of 80%, precision of 81%, recall of 95%, and an F1-Score of 88%. Based on the results of this research, the K-Nearest Neighbors method can be used for sentiment classification of user reviews with good results.

Airline Attitudes: Mining Data for Service Sentiment Insights

Published user reviews and opinions underlie sentiment analysis, thereby enabling extraction of essential data about service providers and indicating their market status. This study investigates smell sentimental grouping precision of standard information mining systems against six Indian carriers related social media microblog datasets. The sentiment analysis with classifiers, namely Bayes Net and SVM, achieved good results in their accuracies. The analysis centers on how well passenger sentiments can be separated out by the six airlines. On WEKA tool, Bayes Net gained the highest accuracy whereas on Rapid Miner tool, SVM yields better classification than all others. A look at passengers experience with the airlines According to reviews, Go Air has been one of the top-knotch in terms of service received by customers and turns out to be highest rated airline based on passenger feedback.

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

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

Boys’ Love (BL) Drama Series Review System for Opinion Mining with Sentiment Analysis

This research portrays same-gender relationships in media, specifically through the drama series of BL (Boys’ Love) and GL (Girls’ Love). Entertainment companies from Thailand, Taiwan, South Korea, Japan, and Vietnam are prominent producers of these series for the global market. While Thai BL dramas have been produced since 2014, they achieved significant popularity and recognition starting in 2017. The proposed system collects user review comments on the BL drama series produced by four countries from 2014 to June 2023. These reviews are compiled into a dataset named H/BL page via Facebook. The dataset comprises 276 series collected from two websites: Dramatist and BLWatcher. Sentiment analysis is utilized to classify user opinions and emotions related to these BL drama series. This technique identifies phrases and emotions within the text, categorizing them as objective (factual), positive (indicating happiness and satisfaction), or negative (indicating unhappiness, disappointment, or rejection) using the Information Gain (IG) method. The system aims to classify opinions on BL drama series and assess public sentiment towards same-gender love.

Assessing Digital Adoption Capability in Public Healthcare Services Through Online User Reviews and Popularity Trends

Assessing Digital Adoption Capability in Public Healthcare Services Through Online User Reviews and Popularity Trends

Enabling Islamic internet-only banks acceptance: an empirical analysis of the UTAUT framework and Islamic compliance

PurposeThe purpose of this study is to explore a theoretical model using the unified theory of acceptance and use of technology framework by adding Islamic compliance (IC) and security assurance to build antecedents of intention to use Islamic internet-only banks (IIOBs) in Muslim youth.Design/methodology/approachThis study involved 262 Muslim youths from several Islamic communities in Indonesia. The analysis was conducted using partial least squares structural equation modelling (PLS-SEM). Moreover, PLS techniques such as the variance accounted for (VAF) were used to verify the statistical analysis of findings.FindingsThe findings show that effort expectancy (EE) and online social influences (OSI) strongly correlate with the intention to use IIOBs. In addition, user reviews (online reviews) are proven to form a solid social influence and impact consumer decision-making. Finally, IC and security assurance are still critical factors in the IIOBs acceptance.Practical implicationsPractically, IIOB providers need to strengthen information about performance, accessibility, security and benefits when running service applications. Providers must show their strengths and advantages compared to the traditional banks in various service lines. Besides, providers are expected to be more careful regarding IC applications in their products and services.Originality/valueThis study considers the effects of OSI, EE, security and IC as the unique characteristics of Muslim youth in using the IIOBs. In particular, the significance of IC and OSIs provides a valid picture of the pathways of acceptance creation towards IIOBs.

Smart EV Charging Station Monitoring and User Feedback System

The efficiency and reliability of electric vehicle (EV) charging stations are pivotal for user satisfaction and broader EV adoption. This research introduces a system that monitors essential parameters of EV charging stations, such as temperature, voltage, and current, using advanced sensors and the ESP32 microcontroller. The collected data is transmitted through the Bylnk platform, for real-time monitoring and analysis. To enhance the user experience, a mobile application has been developed, allowing users to review these critical details and receive notifications. The app provides a detailed overview of each charging station's performance, helping users identify the best stations based on historical data and user reviews. By offering insights into the operational status and efficiency of charging stations, the system aids users in making informed decisions about where to charge their vehicles. This integration of real-time monitoring with user-friendly mobile access improves user convenience and optimizes the EV charging infrastructure management.

Sentiment analysis for movie recommendations: Harnessing opinion mining systems to analyze user reviews

Sentiment analysis for movie recommendations: Harnessing opinion mining systems to analyze user reviews

ALVoyage – An AI Travel Planner

Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.

CareerConnector

Abstract: CareerConnector is an innovative online platform designed to facilitate efficient job searching and recruitment processes, connecting job seekers with potential employers. Powered by a robust technology stack, with .Net serving as the backend language, CareerConnector offers a seamless user experience for both job seekers and employers. Key functionalities encompass user registration and profile creation, advanced job search and filtering, streamlined job posting and application processes, user notifications, and an array of administrative tools for platform management. Additionally, the system incorporates advanced features such as resume upload and parsing, applicant tracking, machine learning-driven job recommendations, and user feedback and review systems.CareerConnector places paramount importance on data privacy and security, adhering to pertinent data protection laws while implementing stringent encryption measures. Scalability and performance optimization are integral to ensuring a smooth and responsive user experience, even as the platform expands. By embracing.Net as the backend language, CareerConnector benefits from its reputation for reliability, scalability, and a wealth of community support. .Net , including React.js, provide the essential infrastructure for handling HTTP requests and database interactions. The ultimate aim of CareerConnector is to create a dynamic, user-centric job portal that effectively bridges the gap between job seekers and employers. By streamlining the job search and recruitment process, it contributes to a more efficient and vibrant

Youtube Comment Sentimental Analysis

The amount of textual data has grown dramatically over time, opening up new avenues for machine learning (ML) and natural language processing (NLP) study. These days, sentiment analysis of comments on YouTube is a really fascinating subject.Although there are a lot of user reviews and comments on many of these films, the low consistency and quality of the material in these comments has prevented much work from being done in terms of identifying trends from them thus far. In this research, we use machine learning techniques and algorithms to perform sentiment analysis on YouTube comments pertaining to popular themes. We show that a clear picture of how real-world events affect public sentiment can be obtained by analyzing the attitudes to identify trends, seasonality, and projections. The findings indicate a strong correlation between the sentiment trends of users and the actual occurrences linked to the corresponding keywords. This study uses a YouTube extractor to perform sentiment analysis on comments on YouTube using citation sentences.To remove the noise from the corpus of comments, various data normalization algorithms were applied to the data. We created a system using six distinct machine learning techniques, including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), to perform classifying on this data set.

Optimal joint strategies of introducing live streaming and online travel agency channels in the tourism supply chain

ABSTRACT With the advent of e-commerce, online travel platforms such as live streaming and online travel agency (OTA) are increasingly developing, which has changed the way people travel and has exerted a significant impact on people’s daily life. In reality, tourist attractions face the challenge of whether introducing live streaming channel, and tour operators face the challenge of whether entering OTAs. To address these challenges, our paper develops theoretical models and presents some insights. The results show that the optimal joint strategy depends on primarily on the fixed fee and annual service fee, but is also affected by other factors such as the size of user traffic, the interactive and review experience, and the consumer's preference. Interestingly, tour operators entering OTAs is always beneficial to tourist attractions, but tourist attractions introducing live streaming is always detrimental to tour operators. Besides, when offline channel and live streaming channel coexist, the total travel package demands will not increase after adding an OTA channel. At last, when considering consumer returns, tourist attractions are more willing to introduce live streaming, but the willingness of tour operators to join OTAs is related to tourists’ acceptance of the OTA channels.

Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls

Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls

Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU

The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score.