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Journal articles on the topic 'User reviews'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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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 the effect-based Kano model. First, the relevant online reviews are selected using various filter mechanisms. Second, UX dimensions (UXDs) are extracted using a proposed method called UX word embedding Latent Dirichlet allocation (UXWE-LDA) and sentiment orientation using a transformer-based pipeline. Then, the casual relationships are identified for the extracted UXDs. Third, the UXDs are mapped on the customer satisfaction model (effect-based Kano) to understand the user perspective about the system, product, or services. Finally, the different parts of the proposed quantification model are evaluated to examine the performance of this method. We present different results of the proposed method in terms of accuracy, topic coherence (TC), Topic-wise performance, and expert-based evaluation for the proposed framework validation. For review quality filters, we achieved 98.49% accuracy for the spam detection classifier and 95% accuracy for the relatedness detection classifier. The results show that the proposed method for the topic extractor module always gives a higher TC value than other models such as WE-LDA and LDA. Regarding topic-wise performance measures, UXWE-LDA achieves a 3% improvement on average compared to LDA due to the incorporation of semantic domain knowledge. We also compute the Jaccard coefficient similarity between the extracted dimensions using UXWE-LDA and UX experts-based analysis for checking the mutual agreement, which is 0.3, 0.5, and 0.4, respectively. Based on the Kano model, the presented study has potential implications concerning issues and knowing the product’s strengths and weaknesses in product design.

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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 seus amigos”, “AutApp Autismo”, “Autismo projeto integrar”, “Jade Autismo”, “Matraquinha”, “OTO (Olhar Tocar Ouvir)” and “Teacch.me”. We conducted an analysis based on the Guidelines for Accessible Interfaces for People with Autism (GAIA) and the BBC Mobile Accessibility Guidelines to classify user reviews.

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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 not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.

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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 unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.

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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 results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest. Findings The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48. Research limitations/implications Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario. Practical implications In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers. Social implications The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications. Originality/value The constructed PCC is novel and was tested on Indian movie review data.

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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 compared with topics generated by Latent Dirichlet Allocation (LDA) and Pachinko Allocation Model (PAM) and shown to be more coherent and interpretable. The authors cut the linkage tree formed while performing the hierarchical clustering of the User-Concerns, at different levels, and generate a hierarchy of User-Concerns. They also discuss how collaborative filtering based recommendation systems can be enriched by infusing additional user-behavioral knowledge from such hierarchy.

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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 lodging such as hotels. It is crucial for guests to find hotels that align with their specific needs and preferences. Additionally, guests are increasingly discerning about their accommodation choices. Therefore, having a system that recommends hotels based on guests' criteria and standards is essential. This project seeks to develop a system that provides valuable hotel recommendations, helping users and guests find accommodations that meet their expectations and requirements. This research paper aims to develop an innovative and user-centric hotel recommender system that utilizes user reviews to assist travelers in finding hotels that best match their preferences. By integrating advanced natural language processing (NLP) techniques and machine learning algorithms, the proposed system will analyze and categorize hotel reviews, allowing users to filter and rank hotels based on specific criteria such as location, amenities, cleanliness, price, and customer satisfaction.

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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 classifying semantically negative user reviews to identify likely malicious, policy violating, or malfunctioning behavior on Alexa skills. ReviewTracker employs a pre-trained FastText classifier to identify different undesired skill behaviors. We collected over 700,000 user reviews spanning 6 years with more than 200,000 negative sentiment reviews. ReviewTracker was able to identify 17,820 reviews reporting violations related to Alexa policy requirements across 2,813 skills, and 131,855 reviews highlighting different types of user frustrations associated with 9,294 skills. In addition, we developed a dynamic skill testing framework using ChatGPT to conduct two distinct types of tests on Alexa skills: one using a software-based simulation for interaction to explore the actual behaviors of skills and another through actual voice commands to understand the potential factors causing discrepancies between intended skill functionalities and user experiences. Based on the number of the undesired skill behavior reviews, we tested the top identified problematic skills and detected more than 228 skills violating at least one policy requirement. Our results demonstrate that user reviews could serve as a valuable means to identify undesired skill behaviors.

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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 ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.

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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 Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

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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%), followed by improvements in precision, recall, and F-measure. SVM with SMOTE is in second place, with 90.50% accuracy and stable performance in other metrics. Naive Bayes with SMOTE shows improvement, but its performance remains slightly below Random Forest and SVM, with an accuracy of 88.80%.

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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 performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors & user trustworthiness, the website from which the user should buy the product will be recommended.

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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 user context information of reviews, and then models the product context information of reviews. Finally, a review rating prediction method that is based on user context and product context is proposed. Our method consists of three main parts. The first part is a global review rating prediction model, which is shared by all users and all products, and it can be learned from training datasets of all users and all products. The second part is a user-specific review rating prediction model, which represents the user’s personalized sentiment information, and can be learned from training data of an individual user. The third part is a product-specific review rating prediction model, which uses training datasets of an individual product to learn parameter of the model. Experimental results on four datasets show that our proposed methods can significantly outperform the state-of-the-art baselines in review rating prediction.

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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 to learn review features from words via a word-level attention, an aspect encoder to learn aspect features via a review-level attention, and a user/item encoder to learn the final representations of users/items via an aspect-level attention. In word- and review-level attentions, we adopt the context-aware mechanism to indicate importance of words and reviews dynamically instead of static attention weights. In addition, the attentions in the word and review levels are of multiple paradigms to learn multiple features effectively, which could indicate the diversity of user/item features. Furthermore, we propose a personalized aspect-level attention module in user/item encoder to learn the final comprehensive features. Extensive experiments are conducted and the results in rating prediction validate the effectiveness of our method.

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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 collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

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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 model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.

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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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Abstract:                      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 mobile panel color was not good and is very weight etc., These kinds of reviews where not useful to the users. In our work we going to categorize the reviews by our probabilistic product ranking algorithm by collecting the user reviews and by polarity based technique we are going to classify the review by noun and phrases for example the speakers prevents the ear from large sound entering in to our ear. These kinds of reviews are positive reviews such as pros and cons (negative part) are also collected such as the mobile has very low battery power. Our probabilistic product ranking algorithm collects or aggregate and give rank to the products and it is very useful to the buyers and sellers. Key words-Online purchase, product aspects, user reviews, probabilistic ranking

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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 performed a regression analysis on 27 apps that had received user reviews. Data on mobile apps' usage rate, volume of user reviews, and user ratings were gathered for this study. The authors also took note of the deficiencies identified by the selected mobile app reviewers. This study's findings revealed a significant relationship between the number of user evaluations and the adoption of mobile accounting apps. However, a significant effect of user reviews on the adoption of mobile accounting apps was not observed. This paper also identifies shortcomings that app users have pointed out in their reviews. It was concluded that Kenya's degree of mobile app adoption has greatly increased due to the volume of app reviews. This study advises entrepreneurs, particularly those who engage with SMEs, to embrace technology and adopt freely downloadable mobile apps for their accounting and bookkeeping requirements.

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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 results showed that there was a strong influence between product reviews on user satisfaction as evidenced by the results of the determination coefficient test of 72.9% of the Female Daily application user satisfaction was determined by product reviews, while the remaining 27.1% was determined by other things outside this study.

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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 produce an accuracy of 85.54% and the most frequently reviewed positive review results are "Good", while the most frequently reviewed negative reviews are "Error". Which indicates a complaint against INFOBMKG users, and from the negative words that appear most often, there are two combinations of the two words that appear most often together, namely the word "very helpful" and the word "less accurate", which indicates that user often complain about problems related to application performance. The results of the sentiment analysis process of testing 3000 review data using the fold = 5 test value in the Support Vector Machine (SVM) method obtained an accuracy of 85.54% which produces predictions on data testing, namely 1500 positive reviews and 1500 negative reviews 1500 reviews.

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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 UX through using online product reviews. A pilot study is conducted to define the key elements of a review. Then, a systematic method of product UX evaluation based on reviews is proposed. The method includes three parts: extraction of key elements, integration of key elements, and quantitative evaluation based on rough number. The effectiveness of the proposed method is demonstrated by a case study using reviews of a wireless vacuum cleaner. Based on the proposed method, designers can objectively evaluate the UX quality of existing products and obtain detailed suggestions for product improvement.

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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 emotions of this population and are increasingly decisive in influencing mHealth app adoption. Objective This study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews. Methods We extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps. Results The analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety. Conclusions Developers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources.

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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 data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises.

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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 algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time). Findings The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM. Originality/value This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

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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 experienced by users, such as technical problems and the need for further personalization. These findings provide valuable insights for online learning application developers to improve the quality of their products and support educational needs in Indonesia. This research also suggests conducting a quantitative study to dig deeper into users' subjective experiences.

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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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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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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 requirement classification and prioritization techniques derived from user reviews. This review revealed that leveraging opinion mining, sentiment analysis, natural language processing, or any stacking technique can significantly enhance the extraction and classification processes. Additionally, an updated matrix taxonomy has been developed based on a combination of definitions from various studies to classify user reviews into four main categories: information seeking, feature request, problem discovery, and information giving. Furthermore, we identified Naive Bayes, SVM, and Neural Networks algorithms as dependable and suitable for requirement classification and prioritization tasks. The study also introduced a new 4-tuple pattern for efficient requirement prioritization, which included elicitation technique, requirement classification, additional factors, and higher range priority value. This study highlights the need for better tools to handle complex user reviews. Investigating the potential of emerging machine learning models and algorithms to improve classification and prioritization accuracy is crucial. Additionally, further research should explore automated classification to enhance efficiency.

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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 external dictionaries are needed for the proposed framework; the only resources needed are the review texts and entity ratings. Second, the framework is applicable across a variety of user-generated content from different domains to construct domain-specific sentiment dictionaries. Finally, each word in the constructed dictionary is associated with a low-dimensional dense representation and a degree of relatedness to a certain rating, which enable us to obtain more fine-grained dictionaries and enhance the application scalability of the constructed dictionaries as the word representations can be adopted for various tasks or applications, such as entity ranking and dictionary expansion. The experimental results on three real-world datasets show that the framework is effective in constructing high-quality domain-specific sentiment dictionaries from customer reviews.

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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, with the evaluation indicator system, we can accurately predict the bad review rate for the product with only 9.9% error. And improvement strategies are proposed after processing the whole approach in the case study. The approach can be applied for product evaluation and improvement, especially for the products with needs for iterative design and sailed online with plenty of user reviews.

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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 User Experience (UX) evaluation can be carried out using the User Experience Questionnaire (UEQ) method, which is an analysis of user experience reviews in order to improve the quality of the game based on six different variables. The review was carried out by distributing an online questionnaire in the form of a Google form consisting of 26 questions and succeeded in collecting 105 respondents. Then, after the analysis was carried out, it was concluded that all aspects of the Clash Of Clans game application received positive evaluation values, namely with a mean value > 0.8 starting from the aspects of Attractiveness, Perspicuity, Efficiency, Dependabilit(Accuracy), Stimulation (Stimulation), and Novelty (Novelty). However, in the Dependability aspect, one of the indicators has a neutral evaluation value, namely the indicator can be predicted or cannot be predicted. This is influential because the resulting mean value is based on the benchmark results graph, namely "below avarege". Based on the analysis that has been carried out on the Clash of Clans game, it can be concluded that in using this application some users are satisfied, but there needs to be improvements to this game by providing more sophisticated security features and providing continuous innovation.

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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 intelligence methods are employed to automatically classify user reviews into relevant NFR categories. This research discusses the performance comparison of the SVM and IndoBERT algorithms in NFR classification. The study involves collecting application review data from 2018 to 2023, sourced from Google Playstore and Apple Appstore, followed by annotating the review data based on ISO 25010. Subsequently, the data is allocated into training and testing sets with an 80:20 ratio. Further, a data preprocessing phase is conducted, which includes steps such as lowercasing, tokenization, special character removal, text normalization, and text stemming. The next step involves training the SVM and IndoBERT algorithms on the dataset. Finally, the evaluation is carried out by calculating the F1-score. The research results indicate that the IndoBERT model outperforms the SVM model. The IndoBERT algorithm excels in recognizing NFR in reviews, achieving an F1-score of 93%, while the SVM algorithm achieves an F1-score of 91%.

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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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Chan, Andrew, Rachel Cohen, Katherine-Marie Robinson, et al. "Evidence and User Considerations of Home Health Monitoring for Older Adults: Scoping Review." JMIR Aging 5, no. 4 (2022): e40079. http://dx.doi.org/10.2196/40079.

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Background Home health monitoring shows promise in improving health outcomes; however, navigating the literature remains challenging given the breadth of evidence. There is a need to summarize the effectiveness of monitoring across health domains and identify gaps in the literature. In addition, ethical and user-centered frameworks are important to maximize the acceptability of health monitoring technologies. Objective This review aimed to summarize the clinical evidence on home-based health monitoring through a scoping review and outline ethical and user concerns and discuss the challenges of the current user-oriented conceptual frameworks. Methods A total of 2 literature reviews were conducted. We conducted a scoping review of systematic reviews in Scopus, MEDLINE, Embase, and CINAHL in July 2021. We included reviews examining the effectiveness of home-based health monitoring in older adults. The exclusion criteria included reviews with no clinical outcomes and lack of monitoring interventions (mobile health, telephone, video interventions, virtual reality, and robots). We conducted a quality assessment using the Assessment of Multiple Systematic Reviews (AMSTAR-2). We organized the outcomes by disease and summarized the type of outcomes as positive, inconclusive, or negative. Second, we conducted a literature review including both systematic reviews and original articles to identify ethical concerns and user-centered frameworks for smart home technology. The search was halted after saturation of the basic themes presented. Results The scoping review found 822 systematic reviews, of which 94 (11%) were included and of those, 23 (24%) were of medium or high quality. Of these 23 studies, monitoring for heart failure or chronic obstructive pulmonary disease reduced exacerbations (4/7, 57%) and hospitalizations (5/6, 83%); improved hemoglobin A1c (1/2, 50%); improved safety for older adults at home and detected changing cognitive status (2/3, 66%) reviews; and improved physical activity, motor control in stroke, and pain in arthritis in (3/3, 100%) rehabilitation studies. The second literature review on ethics and user-centered frameworks found 19 papers focused on ethical concerns, with privacy (12/19, 63%), autonomy (12/19, 63%), and control (10/19, 53%) being the most common. An additional 7 user-centered frameworks were studied. Conclusions Home health monitoring can improve health outcomes in heart failure, chronic obstructive pulmonary disease, and diabetes and increase physical activity, although review quality and consistency were limited. Long-term generalized monitoring has the least amount of evidence and requires further study. The concept of trade-offs between technology usefulness and acceptability is critical to consider, as older adults have a hierarchy of concerns. Implementing user-oriented frameworks can allow long-term and larger studies to be conducted to improve the evidence base for monitoring and increase the receptiveness of clinicians, policy makers, and end users.

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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 33 tourist attractions in Ulsan Metropolitan City, South Korea, were used for learning, where the ratio of irrelevant reviews in the two kinds of data sets was 53.7% and 71.6%, respectively. To deal with skewness in the collected reviews, oversampling algorithms such as SMOTE, Borderline-SMOTE, and ADASYN were used. To build a model for the detection of irrelevant reviews, RNN, LSTM, GRU, and BERT were adopted and compared, as they are known to provide high accuracy in text processing. The performance of the detection models was examined through experiments, and the results showed that the BERT model presented the best performance, with an F1 score of 0.965.

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