User Review Data Research Articles

User Sentiment Analysis Based on Securities Application Elements.

Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.

Sentiment Analysis of the Indriver Online Ojek Application using the Naïve Bayes Classifier Method

According to statista.com, there are 73.1 million online motorcycle taxi users in Indonesia and there are 68.1 million active online motorcycle taxi users in Indonesia especially in the province of North Sumatra, there are 43,811 online motorcycle taxi drivers. The Indriver online motorcycle taxi application is an international online transportation service that gives passengers and drivers the freedom to negotiate prices. Sentiment analysis analizes text to determine positive, negative, or neutral sentiments. The method commonly used in sentiment analysis is the Naive Bayes Classifier method. This research uses quantitative methods to analyze sentiment toward the InDriver online motorcycle taxi application by utilizing the Naïve Bayes Classifier algorithm. User review data is collected from reviews on the Google Play Store, then cleaned and converted into a format suitable for statistical analysis. To analyze sentiment towards the InDriver online motorcycle taxi application using the Naïve Bayes Classifier method, collecting review data and user comments using the Python library and the Visual Studio code application, carrying out preprocessing, TF-IDF weighting, dividing the data into 70% and 30%, after that conducting testing using naïve Bayes classifier algorithm, as well as carrying out evaluation using a confusion matrix. The results of calculating the level of accuracy using the Naïve Bayes method for sentiment classification can be said to be good, this can be seen from the accuracy results on a dataset of 1393 with a comparison of training data and test data of 7:3, obtaining an accuracy value of 76%, precision of 71% , recall of 81% and f1-score of 76%. The results of this research analysis produced superior positive sentiment totaling 677 and negative sentiment totaling 608 while neutral was 90

Automating the Extraction of Words and Topics in Indonesian Using the Term Frequency-Inverse Document Frequency Algorithm and Latent Dirichlet Allocation

Keyword extraction and topic modeling in the analysis of Gojek user reviews in Indonesian are very important. By understanding user preferences and needs through keyword extraction, as well as grouping user reviews into different topics through topic modeling, stakeholders can use the information to further improve services. This research uses TF-IDF and LDA approaches to analyze text data from Gojek user reviews and feedback. The data spans from Nov 5, 2021, to Jan 2, 2024, totaling 225,002 rows. Each row includes username, content, time, and app version. The focus is on content reviews. The average length is 8 words, with a maximum of 104 and a minimum of a few words. The variability indicates a non-normal distribution. Preprocessing is conducted to maintain topic analysis accuracy. The TF-IDF method is used to extract relevant keywords, while the LDA approach is used to model the topics in user reviews. The topic analysis reveals patterns in Gojek user reviews. The first topic discusses experience, services, and affordable pricing. The second emphasizes app usability and benefits. The third relates to promos, discounts, and vouchers. The fourth reflects positive evaluations of service quality. However, the fifth topic highlights high costs and app issues. The sixth underscores overall user satisfaction and service convenience. Testing on the topic model yielded a coherence level of 0.509, indicating that the model's topics demonstrate a good level of consistency in finding relevant topics from Gojek user review data. The use of a combination of TF-IDF and LDA in Indonesian text analysis, particularly in the context of Gojek user reviews, is an important step in enhancing understanding and utilization of text data to improve overall user experience.

Clustering Analysis of Hotel Network Reviews Based on Text Mining Method

With the development of information technology, users use online platforms to post real-time online comments to express their preferences and opinions on goods or services. Online review information expresses users' behavioral habits and special preferences. In depth, analysis of hotel online reviews can improve the adaptability of hotel services to user needs. Effective mining of the vast user review data will provide value for the development of the tourism industry. Using text mining methods to process hotel review data, multiple clustering methods were compared and analyzed for positive and negative feature words from the perspective of user experience. It was found that the k-means++ algorithm had a better clustering effect on user network reviews and achieved better clustering and segmentation of user evaluation information. Unsupervised clustering analysis can be used to further classify online comment information into categories based on positive and negative reviews, providing intellectual support for improving the precision and personalized service quality of hotels.

Eliminating bias: enhancing children's book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization.

Managing user bias in large-scale user review data is a significant challenge in optimizing children's book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children's book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users' book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children's book recommendation systems.

A Quantitative Study on Factors Influencing User Satisfaction of Micro-Mobility in China in the Post-Sharing Era

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users’ willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation.

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Intelligent search engines including pre-trained generative transformers (GPT) have revolutionized the user search experience. Several fields including e-commerce, education, and hospitality are increasingly exploring GPT tools to study user reviews and gain critical insights to improve their service quality. However, massive user-review data and imprecise prompt engineering lead to biased, irrelevant, and impersonal search results. In addition, exposing user data to these search engines may pose privacy issues. Motivated by these factors, we present ChatReview, a ChatGPT-enabled natural language processing (NLP) framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity. We analyze our framework using three real-world domains including education, local restaurants, and hospitality. We assert that our framework simplifies prompt engineering for general users to produce effective results while minimizing the exposure of sensitive user data to search engines. We also present a one-of-a-kind Large Language Model (LLM) peer assessment of the ChatReview framework. Specifically, we employ Google’s Bard to objectively and qualitatively analyze the various ChatReview outputs. Our Bard-based analyses yield over 90% satisfaction, establishing ChatReview as a viable survey analysis tool.

Analisis Topic Modelling pada Ulasan Aplikasi Shopee di PlayStore Menggunakan Latent Direchlet Allocation (LDA)

Shopee is a popular e-commerce platform in Indonesia. To improve service quality, data analysis is needed to understand user responses and preferences. This study aims to conduct data analysis and topic modelling using the Latent Dirichlet Allocation (LDA) method on 33,896 user review data of the Shopee application on PlayStore. LDA modelling was performed by considering the parameters passes and iterations. Passes with values of 5, 10, 15, and 20 were tested with a combination of iterations values of 50 and 100 and a random state of 142. During the testing, it was found that the highest average coherence score was achieved by using passes 10 and iterations 50. Therefore, passes 10 and iterations 50 were selected as the final LDA model parameters, resulting in seven topics. The topics that emerged from the analysis include user satisfaction, shipping and product suitability, user trust, user experience, service and support, application, and promotional offers. The "application" topic was found to be the most critical and received the highest number of rating 1. High-weighted words on the application topic such as "slow," "heavy," and "crash" indicate that issues related to the application's system performance and functionality need to be addressed and improved.

Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application

Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.

Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.

S-DeepTrust: A deep trust prediction method based on sentiment polarity perception

In online social networks, trust prediction is crucial for applications such as user decision-making and high-impact user recommendation. In recent years, various solutions to the trust prediction problem have been proposed in many academic literatures. However, the existing methods only focus on using more available information to achieve better prediction performance, while ignoring the influence of the authenticity of user information and the user subjectivity. In this paper, we apply the sentiment labels to the trust prediction process for the first time, and propose a deep trust prediction method based on sentiment polarity perception, S-DeepTrust. We first use transfer learning to generate sentiment labels for user review data and obtain a sentiment polarity matrix. Next, we design a rating matrix generation algorithm, which weights the sentiment polarity matrix and the original rating matrix to produce a new rating matrix. Then, the Siamese network architecture is used for the first time in the field of trust prediction to judge the trust relationships based on the extracted user preference vectors, which minimizes the representation of trust user pairs. Finally, the experiment results show that our method achieves higher prediction accuracy and is robust to trust relation sparsity compared with existing methods.

Sentiment Analysis on App Reviews Using Support Vector Machine and Naïve Bayes Classification

A review is an assessment given by someone based on certain aspects, such as the delivery of stories, pictures, effects, or visuals. Users can provide reviews which help the company know the quality of the application. However, reviews cannot be used as a reference for rating, because there are still users who provide reviews that are irrelevant to the rating given. This study aims to carry out sentiment analysis in order to classify the application user review data. The sentiment classification process begins with collecting and labeling 700 data. The data then goes through a text preprocessing, word weighting with TF-IDF, and classification using the Support Vector Machine and Naïve Bayes Classifier. The results produce the highest accuracy in the comparison of training and test data of 90%:10%. Support Vector Machine algorithm is capable of providing high accuracy with RBF kernel, γ=1, and C=10. The results obtained using 10-fold cross validation give an accuracy value of 92.86%, a precision value of 92.88%, a recall value of 92.88%, a specificity value of 94.73%, and f-measure of 92.76%. Naïve Bayes Classifier method is able to provide high accuracy by using Multinomial Naïve Bayes Classifier. The results obtained using 10-fold cross validation give an accuracy value of 92.54%, a precision value of 92.55%, a recall value of 92.51%, a specificity value of 93.9%, and f-measure of 92.44%. Based from the result, it can be concluded that the classification using the Support Vector Machine is superior to the Naive Bayes Classification.

Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named MPIO-BDL model for SA on WI applications. The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency (TF-IDF) technique to derive a useful set of information from the user reviews. Besides, the MPIO-FS model is applied for the selection of optimal feature subsets, which helps to enhance classification accuracy and reduce computation complexity. Moreover, the BDL model is employed to allocate the proper class labels of the applied user review data. A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.

ANALISIS SENTIMEN PADA ULASAN APLIKASI AMAZON SHOPPING DI GOOGLE PLAY STORE MENGGUNAKAN NAIVE BAYES CLASSIFIER

Sentiment analysis or opinion mining is a study that analyzes people's opinions, thoughts and impressions on various topics, subjects, and products or services. The development of social media makes public opinion data available which can be found easily on the internet. The large volume of data causes the need for an automatic system to classify the data based on different aspects because classifying data manually is a time-consuming process. In this study, sentiment analysis will be carried out with a machine learning-based approach using the Naive Bayes algorithm using user review data on the Amazon Shopping application on the Google Play Store. The classification results using the four Naive Bayes algorithms produce an average accuracy of 82.15%, precision of 72.25%, recall of 83.49%, and f1-score of 77.41%. Multinomial NB produces the best accuracy among the four Naive Bayes algorithms used, which is 86.74%. The values of precision, recall, and f1-score are 78.82%, 85.90%, and 82.21%, respectively.

Summarizing Online Customer Review using Topic Modeling and Sentiment Analysis

With the massive implementation of social media in various forms in various business domains, business or product owners have the opportunity to be able to take advantage of user review data that is available free of charge to evaluate the products they issue. User reviews on social media platforms, marketplaces, and e-commerce are User Generated Content (UGC) which is very useful for product owners to find out the extent of user preferences for their products. However, to be able to comprehensively read the data, the right technology is needed considering that the data is in the form of text in very large quantities. Reading one by one and then drawing conclusions is certainly not the right approach because it will take quite a lot of time. So, in this study, the researcher will use a text analysis-based approach, especially topic modeling and sentiment analysis to summarize user reviews in the comments or reviews column on the e-commerce platform. The case study used in this study is user reviews in the comments column on the Amazon site for the Lenovo K8 Note smartphone product. From the experiments carried out, the approach used can summarize the reviews written by quite many users in one summary that can be easily understood.

Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia

Opensea is an NFT buying and selling application-based platform that is booming in the community. One way to find out the public's perception of the Opensea application is by sentiment analysis, as done in this study. Data that is used is user review data for the Opensea application in the Indonesian play store. The sentiment analysis technique used is the Naïve Bayes Classifier and the Support Vector Machine (SVM) method. Both are used to compare public responses from sentiment analysis of reviewed data labeled as positive, negative, and neutral. Based on this study, it was found that the Naive Bayes algorithm gives the results that class precision is 87.31%, class recall is 71.02%, and accuracy is 89.81%. While the SVM algorithm gives the results that class precision is 94.23%, class recall 71.96%, and Accuracy 90.78%. It is concluded that the SVM algorithm has a better performance than the Naive Bayes algorithm.

Sentiment Analysis of JNE User Perception using Naïve Bayes Classifier Algorithm

The logistics industry is growing very rapidly. One of big industry in Indonesia is PT. Tiki Line Nugraha Ekakurir (JNE), which has been established for 29 years. This company has an extensive network in all cities in Indonesia, with service points of 1,500 locations. JNE has an application called my JNE on Google Play, which received more than 86,000 reviews and since December 2019 only got a rating of 2.4 stars out of a total rating of 5 stars. This study is obtained to analysis JNE user review data from Google Play. The reviews used in this study totaled 1,876 classified into positive and negative sentiment classes using the Naïve Bayes Classifier algorithm and word associations were also implemented. Classification with naïve bayes classifier with 90% training data and 10% test data had the best accuracy of 85.87%. Furthermore, for the text association, information is obtained that JNE users are talking about "send", "package", "courier", "good", "application", "fast", "service", "receive", "help", and "star". Whereas in the class of negative sentiment users often talk about "send", "package", "courier", "disappointed", "service", "service", "bad", "application", "severe", and "slow".

User preference mining based on fine-grained sentiment analysis

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.

Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis

As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is ​​obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.

Opinion Mining-Based Term Extraction Sentiment Classification Modeling

The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.