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Journal articles on the topic 'User reviews'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hussain, Jamil, Zahra Azhar, Hafiz Farooq Ahmad, Muhammad Afzal, Mukhlis Raza, and Sungyoung Lee. "User Experience Quantification Model from Online User Reviews." Applied Sciences 12, no. 13 (2022): 6700. http://dx.doi.org/10.3390/app12136700.

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Due to the advancement in information technology and the boom of micro-blogging platforms, a growing number of online reviews are posted daily on product distributed platforms in the form of spontaneous and insightful user feedback, and these can be used as a significant data source to understand user experience (UX) and satisfaction. However, despite the vast amount of online reviews, the existing literature focuses on online ratings and ignores the real textual context in reviews. We proposed a three-step UX quantification model from online reviews to understand customer satisfaction using t

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Zhou, Wenqi, and Wenjing Duan. "Do Professional Reviews Affect Online User Choices Through User Reviews? An Empirical Study." Journal of Management Information Systems 33, no. 1 (2016): 202–28. http://dx.doi.org/10.1080/07421222.2016.1172460.

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Santiago, Mara Taynar, and Anna Beatriz Marques. "Exploring user reviews to identify accessibility problems in applications for autistic users." Journal on Interactive Systems 14, no. 1 (2023): 317–30. http://dx.doi.org/10.5753/jis.2023.3238.

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The Google Play Store provides various user reviews that can provide information about user experience, usability, and accessibility. Despite multiple studies addressing these reviews’ importance and contributions to improving interactive systems, accessibility for users with Autism Spectrum Disorder (ASD) is still little discussed in this context. Considering the potential of user reviews, this article presents a textual analysis of reviews extracted from eight educational applications available in Portuguese with a focus on autistic children, namely: “ABC Autismo”, “Aprendendo com Biel e seu

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J.S, Ravikumar, Narayana Reddy T, and Syed Mohammad Ghouse. "“User Generated Reviews and Business Promotions”." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 1619–29. http://dx.doi.org/10.37200/ijpr/v24i2/pr200464.

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Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained n

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Alnusyan, Ruba, Ruba Almotairi, Sarah Almufadhi, Amal A. Al-Shargabi, and Jowharah F. Alshobaili. "Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (2022): 79–93. http://dx.doi.org/10.3991/ijim.v16i08.30169.

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Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and

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Alnusyan, Ruba, Ruba Almotairi, Sarah Almufadhi, Amal A. Al-Shargabi, and Jowharah F. Alshobaili. "Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (2022): 79–93. http://dx.doi.org/10.3991/ijim.v16i08.30169.

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Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and

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Mcfarlane, G. W. P. "Book Reviews : User-Friendl y tHeology." Expository Times 110, no. 9 (1999): 303. http://dx.doi.org/10.1177/001452469911000924.

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Carey, Tom. "Video reviews: USER INTERFACE STRATEGIES '88." ACM SIGCHI Bulletin 21, no. 2 (1989): 128–30. http://dx.doi.org/10.1145/70609.1047718.

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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "Analysing user sentiment of Indian movie reviews." Electronic Library 36, no. 4 (2018): 590–606. http://dx.doi.org/10.1108/el-08-2017-0182.

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Purpose To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews. Design/methodology/approach An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the re

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Pradhan, Ligaj, Chengcui Zhang, and Steven Bethard. "Extracting Hierarchy of Coherent User-Concerns to Discover Intricate User Behavior from User Reviews." International Journal of Multimedia Data Engineering and Management 7, no. 4 (2016): 63–80. http://dx.doi.org/10.4018/ijmdem.2016100104.

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Intricate user-behaviors can be understood by discovering user interests from their reviews. Topic modeling techniques have been extensively explored to discover latent user interests from user reviews. However, a topic extracted by topic modelling techniques can be a mixture of several quite different concepts and thus less interpretable. In this paper, the authors present a method that uses topic modeling techniques to discover a large number of topics and applies hierarchical clustering to generate a much smaller number of interpretable User-Concerns. These User-Concerns are further compare

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Paranjape, Vishal, Saurabh Sharma, and Abhishek Singh. "Developing a User-Friendly Hotel Recommendation System Based on User Reviews." International Journal of Innovative Research in Computer and Communication Engineering 10, no. 02 (2022): 671–76. http://dx.doi.org/10.15680/ijircce.2022.1002051.

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The hospitality sector is a significant global industry, encompassing restaurants, hotels, parks, cruises, and various entertainment services worldwide. Given its role in providing entertainment, relaxation, and tourism opportunities, it holds considerable importance for people everywhere. This project aims to focus specifically on the hotel industry, a key subcategory within hospitality. The hotel industry is dedicated to offering accommodation services to a diverse range of guests. Vacations are popular activities for individuals, couples, and families, typically involving some form of lodgi

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Aldeen, Mohammed, Jeffrey Young, Song Liao, et al. "End-Users Know Best: Identifying Undesired Behavior of Alexa Skills Through User Review Analysis." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (2024): 1–28. http://dx.doi.org/10.1145/3678517.

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The Amazon Alexa marketplace has grown rapidly in recent years due to third-party developers creating large amounts of content and publishing directly to a skills store. Despite the growth of the Amazon Alexa skills store, there have been several reported security and usability concerns, which may not be identified during the vetting phase. However, user reviews can offer valuable insights into the security & privacy, quality, and usability of the skills. To better understand the effects of these problematic skills on end-users, we introduce ReviewTracker, a tool capable of discerning and

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Hu, Shuyue, Yi Cai, Ho-fung Leung, Dongping Huang, and Yang Yang. "Integrating User Reviews and Ratings for Enhanced Personalized Searching." International Journal of Distance Education Technologies 15, no. 2 (2017): 86–101. http://dx.doi.org/10.4018/ijdet.2017040106.

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With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result r

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Patel, Nimesh V., and Hitesh Chhinkaniwala. "Investigating Machine Learning Techniques for User Sentiment Analysis." International Journal of Decision Support System Technology 11, no. 3 (2019): 1–12. http://dx.doi.org/10.4018/ijdsst.2019070101.

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Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machin

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Wang, Yihui, Shanquan Gao, Yan Zhang, Huaxiao Liu, and Yiran Cao. "UISMiner: Mining UI suggestions from user reviews." Expert Systems with Applications 208 (December 2022): 118095. http://dx.doi.org/10.1016/j.eswa.2022.118095.

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Li, Yuanchun, Baoxiong Jia, Yao Guo, and Xiangqun Chen. "Mining User Reviews for Mobile App Comparisons." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, no. 3 (2017): 1–15. http://dx.doi.org/10.1145/3130935.

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Liu, Wei, Hualiang Yan, and Jianguo Xiao. "Automatically extracting user reviews from forum sites." Computers & Mathematics with Applications 62, no. 7 (2011): 2779–92. http://dx.doi.org/10.1016/j.camwa.2011.07.044.

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Wu, Canrui, Chen Wang, Yipeng Zhou, et al. "Exploiting user reviews for automatic movie tagging." Multimedia Tools and Applications 79, no. 17-18 (2020): 11399–419. http://dx.doi.org/10.1007/s11042-019-08513-0.

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Pu, Xiaojia, Gangshan Wu, and Chunfeng Yuan. "User-aware topic modeling of online reviews." Multimedia Systems 25, no. 1 (2017): 59–69. http://dx.doi.org/10.1007/s00530-017-0557-6.

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Bilmes, J. "User-friendly neural-net design [software reviews]." IEEE Spectrum 33, no. 2 (1996): 63. http://dx.doi.org/10.1109/mspec.1996.482277.

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Marcella, Albert. "Application Reviews of End-User Spreadsheet Designs." EDPACS 17, no. 4 (1989): 10–14. http://dx.doi.org/10.1080/07366988909450563.

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Wicaksana, Endra Maulia, and Nova Rijati. "Analyzing Sentiment of SiCepat Express User Reviews." Journal of Applied Informatics and Computing 9, no. 1 (2025): 235–40. https://doi.org/10.30871/jaic.v9i1.8056.

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The development of e-commerce in Indonesia has led to an increase in the number of users of product delivery services to deliver their customers' orders to their destination. SiCepat Ekspres is the number one fastest delivery service in Indonesia, besides JNE and JNT Express. The study aims to evaluate the performance of sentiment analysis methods in identifying and classifying sentiments related to SiCepat Ekspres. Data from Twitter media as many as 10,000 dataset records. The experimental results show that Random Forest with SMOTE is the best method, as it has the highest accuracy (91.10%),

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Soor, Gunjeet Kaur, Amey Morje, Rohit Dalal, and Deepali Vora. "Product Recommendation System based on User Trustworthiness & Sentiment Analysis." ITM Web of Conferences 32 (2020): 03030. http://dx.doi.org/10.1051/itmconf/20203203030.

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The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and p

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Wang, Bingkun, Shufeng Xiong, Yongfeng Huang, and Xing Li. "Review Rating Prediction Based on User Context and Product Context." Applied Sciences 8, no. 10 (2018): 1849. http://dx.doi.org/10.3390/app8101849.

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With the explosion of online user reviews, review rating prediction has become a research focus in natural language processing. Existing review rating prediction methods only use a single model to capture the sentiments of review texts, ignoring users who express the sentiment and products that are evaluated, both of which have great influences on review rating prediction. In order to solve the issue, we propose a review rating prediction method based on user context and product context by incorporating user information and product information into review texts. Our method firstly models the u

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Liu, Hongtao, Wenjun Wang, Qiyao Peng, Nannan Wu, Fangzhao Wu, and Pengfei Jiao. "Toward Comprehensive User and Item Representations via Three-tier Attention Network." ACM Transactions on Information Systems 39, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3446341.

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Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder

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Wang, Heyong, Ming Hong, and Jinjiong Lan. "Study on Collaborative Filtering Recommendation Model Fusing User Reviews." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 5 (2019): 864–73. http://dx.doi.org/10.20965/jaciii.2019.p0864.

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The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collabor

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Utami, Theresia Arwila. "Sentiment Analysis of Hotel User Review using RNN Algorithm." International Journal of Informatics and Computation 3, no. 1 (2021): 30. http://dx.doi.org/10.35842/ijicom.v3i1.34.

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Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our mod

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Karthik, M., and M. Govindaraj. "RECOGNIZING TEXTUAL POLARITY IN REVIEWS OF THE PRODUCTS TO RANKING ITS ASPECTS." International Journal of Advances in Engineering & Scientific Research 2, no. 4 (2015): 13–19. https://doi.org/10.5281/zenodo.10726212.

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<strong><em>Abstract</em><em>: </em></strong> <em>                    </em> <em>Analyzing consumer reviews for purchasing products in online are the important fact today. Those reviews should useful to the buyers. In existing system these reviews were not correctly classified and it is not useful to the buyers. For example for a particular product a user wants to buy a mobile in online means he checks the reviews of the particular mobile some existing user reviews were like that particular mobil

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Gebauer, Judith, Ya Tang, and Chaiwat Baimai. "User requirements of mobile technology: results from a content analysis of user reviews." Information Systems and e-Business Management 6, no. 4 (2007): 361–84. http://dx.doi.org/10.1007/s10257-007-0074-9.

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Kamau, Charles Guandaru, Juliana Hawario Asser, Mary Penina Ibua, and Isaac Ojung'a Otiende. "Adoption of accounting mobile apps in Kenya: The effect of user reviews and user ratings." Journal of Accounting, Business and Finance Research 16, no. 1 (2023): 36–43. http://dx.doi.org/10.55217/102.v16i1.632.

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Modern industry improves anthropogenic activities and greatly simplifies human effort and the industrial world. Cloud computing and mobile applications are more than just buzzwords; they are crucial elements of how business is conducted and how it will be conducted in the future. A rising number of SMEs are currently utilizing mobile and cloud computing technology. The purpose of this paper is to analyze the linkages between user reviews and ratings and the adoption of mobile accounting apps among SMEs in Kenya. The study collected data on 35 commonly used mobile accounting applications and pe

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Amienah Atthahirah. "Influence Of Product Reviews on User Satisfaction of the Female Daily Application." Jurnal Manajemen Bisnis Eka Prasetya Penelitian Ilmu Manajemen 9, no. 2 (2023): 140–49. http://dx.doi.org/10.47663/jmbep.v9i2.319.

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Product reviews currently have an important role in knowing new information and becoming a consideration that will later become a purchasing decision. Many platforms accommodate a collection of information about product information as a potential fulfillment of user expectations. Is the product review able to fulfill user Female Daily Application's expectations? The purpose of this study is to determine whether product reviews have an influence on user satisfaction of the Female Daily application. The reasearch method used is a quantitative and data processing using SPSS version 27.0. The resu

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Ilham Fannani, Enggar Novianto, and Alfin Syarifuddin Syahab. "User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm." Inspiration: Jurnal Teknologi Informasi dan Komunikasi 13, no. 1 (2023): 48–58. http://dx.doi.org/10.35585/inspir.v13i1.42.

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On the Google Play Store, users often read other users' app reviews and reputations, before downloading an app. This makes the analysis of user reviews very interesting for app owners to make future decisions. This study aims to analyze user reviews of the Info BMKG application on the Google Play Store, using sentiment analysis. This user review analysis uses the Support Vector Machine (SVM) method. The evaluation proposal was made from more than 3,000 user reviews collected from the INFOBMKG application on the Google Play Store. The results of the analysis using the Support Vector Machine pro

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Luhur, Tri, Rafika Rahmawati, Tri Puspa Rinjeni, Virdha Rahma Aulia, Prasasti Karunia Farista Ananto, and Iqbal Ramadhani Mukhlis. "Analyzing User Reviews with ETL using Pentaho Data Integration." Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) 4, no. 2 (2025): 451–58. https://doi.org/10.62712/juktisi.v4i2.403.

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User reviews are a crucial element in guiding the continuous improvement of mobile applications for developers. This research aims to utilize Extract, Transform, Load (ETL) techniques using Pentaho Data Integration to analyze user reviews of government mobile applications which is ‘Sentuh Tanahku’, focusing on improving service quality through actionable data insights. The ETL process involves collecting and cleaning data from the Google Play Store to derive valuable insights that inform recommendations for app improvement. After data extraction, text preprocessing steps, such as cleansing, ca

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Cheng, Fangmin, Suihuai Yu, Shengfeng Qin, Jianjie Chu, and Jian Chen. "User experience evaluation method based on online product reviews." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1791–805. http://dx.doi.org/10.3233/jifs-210564.

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Evaluating the quality of the user experience (UX) of existing products is important for new product development. Conventional UX evaluation methods, such as questionnaire, have the disadvantages of the great subjective influence of investigators and limited number of participants. Meanwhile, online product reviews on e-commerce platforms express user evaluations of product UX. Because the reviews objectively reflect the user opinions and contain a large amount of data, they have potential as an information source for UX evaluation. In this context, this study explores how to evaluate product

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Meyer, Julien, and Senanu Okuboyejo. "User Reviews of Depression App Features: Sentiment Analysis." JMIR Formative Research 5, no. 12 (2021): e17062. http://dx.doi.org/10.2196/17062.

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Background Mental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotio

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Grimaldi, Didier, Carly Collins, and Sebastian Garcia Acosta. "Dynamic Restaurants Quality Mapping Using Online User Reviews." Smart Cities 4, no. 3 (2021): 1104–12. http://dx.doi.org/10.3390/smartcities4030058.

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Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR dat

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of. Supriya Sarkar, Vanashri N. Sawant, Pr. "Logistic Regression: Aggregating Reviews by User Preference Modeling." International Journal of Innovative Research in Computer and Communication Engineering 3, no. 8 (2015): 7293–301. http://dx.doi.org/10.15680/ijircce.2015.0308023.

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Trivedi, Shrawan Kumar, Shubhamoy Dey, and Anil Kumar. "Capturing user sentiments for online Indian movie reviews." Electronic Library 36, no. 4 (2018): 677–95. http://dx.doi.org/10.1108/el-04-2017-0075.

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Purpose Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers. Design/methodology/approach In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithm

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Noerhartati, Endang, and Narariya Dita Handani. "Analysis of User Reviews of Online Learning Applications." TA'DIBUNA: Jurnal Pendidikan Agama Islam 7, no. 1 (2024): 21. https://doi.org/10.30659/jpai.7.1.21-30.

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This study aims to analyze user reviews of Muslim students of the Ruangguru online learning application in Indonesia using word frequency and co-occurrence analysis methods from March 2020 to February 2023. By applying a qualitative approach to extensive review data, this study identifies the factors that influence satisfaction and user preferences in the online learning process. The results show that usability, content quality, interactivity, and technical support are important aspects that contribute to the user learning experience of Muslim students. However, there are also challenges exper

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Zhang, Jianzhang, Jialong Zhou, Jinping Hua, Nan Niu, and Chuang Liu. "Mining user privacy concern topics from app reviews." Journal of Systems and Software 222 (April 2025): 112355. https://doi.org/10.1016/j.jss.2025.112355.

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Rossetti, Marco, Fabio Stella, and Markus Zanker. "Analyzing user reviews in tourism with topic models." Information Technology & Tourism 16, no. 1 (2015): 5–21. http://dx.doi.org/10.1007/s40558-015-0035-y.

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Salleh, Amran, Mar Yah Said, Mohd Hafeez Osman, and Sa’adah Hassan. "A Review on Classifying and Prioritizing User Review-Based Software Requirements." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1651. https://doi.org/10.62527/joiv.8.3-2.3450.

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User reviews are a valuable source of feedback for software developers, as they contain user requirements, opinions, and expectations regarding app usage, including dislikes, feature requests, and reporting bugs. However, extracting and analyzing user requirements from user reviews is ineffective due to the large volume, unstructured nature, and varying quality of the reviews. Therefore, further research is not just necessary but crucial to effectively explore methods to gather informative and meaningful user feedback. This study aims to investigate, analyze, and summarize the methods of requi

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Wang, Chun-Hsiang, Kang-Chun Fan, Chuan-Ju Wang, and Ming-Feng Tsai. "UGSD: User Generated Sentiment Dictionaries from Online Customer Reviews." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 313–20. http://dx.doi.org/10.1609/aaai.v33i01.3301313.

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Customer reviews on platforms such as TripAdvisor and Amazon provide rich information about the ways that people convey sentiment on certain domains. Given these kinds of user reviews, this paper proposes UGSD, a representation learning framework for constructing domain-specific sentiment dictionaries from online customer reviews, in which we leverage the relationship between user-generated reviews and the ratings of the reviews to associate the reviewer sentiment with certain entities. The proposed framework has the following three main advantages. First, no additional annotations of words or

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Zubair Asghar, Muhammad, Maria Qasim, Bashir Ahmad, Shakeel Ahmad, Aurangzeb Khan, and Imran Ali Khan. "Health miner: opinion extraction from user generated health reviews." International Journal of Academic Research 5, no. 6 (2013): 279–84. http://dx.doi.org/10.7813/2075-4124.2013/5-6/a.35.

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Yang, Cheng, Lingang Wu, Kun Tan, et al. "Online User Review Analysis for Product Evaluation and Improvement." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 5 (2021): 1598–611. http://dx.doi.org/10.3390/jtaer16050090.

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Traditional user research methods are challenged for the decision-making in product design and improvement with the updating speed becoming faster, considering limited survey scopes, insufficient samples, and time-consuming processes. This paper proposes a novel approach to acquire useful online reviews from E-commerce platforms, build a product evaluation indicator system, and put forward improvement strategies for the product with opinion mining and sentiment analysis with online reviews. The effectiveness of the method is validated by a large number of user reviews for smartphones wherein,

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AlHafiidh, Agung, and Nabila Rizky Oktadini. "Analisis User Experience (UX) Pada Aplikasi Game Clash Of Clans Menggunakan Metode User Experience Questionnaire (UEQ)." INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System 8, no. 2 (2023): 219. http://dx.doi.org/10.51211/isbi.v8i2.2694.

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Information technology is currently developing very rapidly and meets people's needs to make work easier, fulfill entertainment needs, and even fulfill sports needs among people in the era of the industrial revolution generation 4.0. What is currently popular is Clash of clans or better known as "CoC". One game that is quite popular among young people. Based on 54.9 million reviews on Android devices and 2.3 million reviews on iOS devices, it still gets negative reviews from CoC users themselves who are not satisfied with the features or security of the game itself. From the problems above, a

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Rais Kumar, Abdul Ghofur, Yudi Sukmono, and Aji Ery Burhandenny. "COMPARISON OF SUPPORT VECTOR MACHINE AND INDOBERT IN NON-FUNCTIONAL REQUIREMENT CLASSIFICATION OF APPLICATION USER REVIEWS." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 1035–42. https://doi.org/10.52436/1.jutif.2024.5.4.1424.

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User reviews of mobile applications have become a valuable source of information for evaluating the quality of an application. It is crucial for application developers to understand what users express in their reviews. One aspect that can be analyzed from user reviews is Non-Functional Requirement (NFR). Classifying reviews based on NFR is essential in understanding how an application can be enhanced. Although user reviews have the potential to provide valuable insights into NFR, manually processing thousands of user reviews is a laborious and inefficient task. Therefore, artificial intelligen

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K., Anita, and M. G. "Summarization and Negative Reviews Opinion Mining of Multiple User Reviews in Text Domain." International Journal of Computer Applications 145, no. 13 (2016): 31–33. http://dx.doi.org/10.5120/ijca2016910885.

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Kim, Cheolgi, and Hyeon Gyu Kim. "Efficient Detection of Irrelevant User Reviews Using Machine Learning." Applied Sciences 14, no. 16 (2024): 6900. http://dx.doi.org/10.3390/app14166900.

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User reviews such as SNS feeds and blog writings have been widely used to extract opinions, complains, and requirements about a given place or product from users’ perspective. However, during the process of collecting them, a lot of reviews that are irrelevant to a given search keyword can be included in the results. Such irrelevant reviews may lead to distorted results in data analysis. In this paper, we discuss a method to detect irrelevant user reviews efficiently by combining various oversampling and machine learning algorithms. About 35,000 user reviews collected from 25 restaurants and 3

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