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24 results for “User Reviews”
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
SURF: Replication Package for: "What Would Users Change in My App? Summarizing App Reviews for Recommending Software Changes"
<p>Description of the content of folder "SURF_replication_package": 1) "Experiment I" contains: a) the folder "summaries" which contains all the html summaries generated through SURF and browsed by study participants involved in the Experiment I. b) the folder "XMLreviews" which contains, for each of the apps involved in the Experiment I, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input files for the SURF tool for generating the summaries contained in the "summaries" folder c) "Experiment_I_results.xlsx" which contains all the answers to our survey collected from the Experiment I participants.</p> <p>2) "Experiment II" contains: a) the folder "summaries" which contains the two html summaries generated through SURF and browsed by study participants in the Experiment II. b) the folder "XMLreviews" which contains, for each of the two apps involved in the Experiment II, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input of the SURF tool for generating the summaries contained in the "summaries" folder. c) "Experiment_II_results.xlsx" which contains all the user feedbacks extracted/validated by survey participants in the two sub-experiments. d) "Experiment_II_survey_answers.xlsx" which contains all the answers to our survey collected in the Experiment II participants.</p> <p>3) "Survey.pdf" which contains the pdf version of the survey performed by the participants</p> <p>4) "SURF_tool.zip" contains: a) "SURF.jar", which contains the class files of a prototypical implementation of SURF b) "README.txt" which contains the instructions to run the SURF tool c) the "lib" folder, which contains all the java libraries needed for running SURF.</p>
User participation in digital accessibility evaluations: reviewing methods and objectives
<p><span>Although laws and standardization bodies promote user participation in digital accessibility evaluations, people with disabilities still consider themselves excluded from this process. One reason could be the lack of systematized knowledge about evaluation methods involving users. This article seeks to understand how and for what purpose digital accessibility evaluations with user participation were conducted in the scientific literature from 2018 to 2021. Three types of user participation emerged: 1) user-based usability testing to evaluate task accomplishment, user reactions and interface qualities; 2) interviewing users to assess the local and social factors impacting digital service accessibility; 3) using questionnaires or crowdsourcing to check the compliance of certain interfaces with accessibility standards. Participants are primarily chosen based on their functional impairments and, to a lesser degree, their project-related skills, biographical information, technology habits, among other criteria. The comprehensive user insights gained with these methods are judged to be positive whereas the lack of representativeness of the selected user samples is found to be regrettable. The article finally discusses the definitions of accessibility and disability that underpin these methodologies.</span></p>
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
<p>6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.</p> <ul> <li><strong>Ratings</strong> are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li><strong>Bug reports</strong> show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li><strong>Feature requests</strong> reflect the demande of users on new function, new content, new interface, etc.</li> <li>In <strong>user experience</strong>, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> <p>As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.</p> <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table> <p> </p> <h2>New Dataset</h2> <p>Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a></p>
User Reviews of nine Social VR applications
<p>These user reviews are collected from nine social VR applications on two digital distributation platforms (Steam and Oculus). All these are in English.</p> <p>Five social VR applications are from Steam: VRChat, Rec Room, PokerStars VR, Altspace, VR, Sansar.</p> <p>Eight social VR applications are from Oculus: VRChat, Rec Room, Echo VR, PokerStars VR, Real VR Fishing, Poker VR, Altspace VR, vTime VR.</p>
Unveiling Competition Dynamics in Mobile App Markets through User Reviews
<p>This replication package contains the datasets and evaluation results for the research titled <i>"<strong>Unveiling Competition Dynamics in Mobile App Markets through User Reviews"</strong>, </i>by Quim Motger, Xavier Franch, Vincenzo Gervasi and Jordi Marco.</p><p>Latest version of the full code is available at: <a href="https://github.com/quim-motger/app-market-analysis">https://github.com/quim-motger/app-market-analysis</a></p>
Artifacts for "Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls"
<p>List of keywords used to build the text classifier for the paper <em>Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls</em>. For more information on how these keywords were obtained, see the "Data Labeling" section of this paper.</p> <p>The provided CSV file contains 3 columns:</p> <ul> <li>"keyword": Lowercased keyword in either English, Russian, Ukranian or Polish.</li> <li>"weight": Score indicating the keyword's war-related affinity, with 3 being the most related. Negative weights are used to correct for exceptions in our classifier.</li> <li>"topic": Subject of the keyword used for topic analysis.</li> </ul>
Automatic User Story Generation: A Comprehensive Systematic Literature Review - Data Extraction
<p>This document presents the data extraction performed for the Systematic Literature Review in Automatic User Story Generation.</p>
The effect of wheelchair users on the egress time of pedestrian crowds: a systematic literature review and meta-analysis
<p>This dataset provides supplementary input data for a systematic literature review and meta-analysis, examining the effects of mobility-impaired individuals, specifically wheelchair users, on pedestrian egress times. It includes the following variables:</p> <ul> <li><strong>short_trial_name</strong>: A unique identifier for each trial.</li> <li><strong>independent variables</strong>: Factors such as the number of attendees, bottleneck width, and mobility profiles (e.g., individuals with or without wheelchair usage).</li> <li><strong>left_shifted_time</strong>: Standardized start time for egress, adjusted for comparability across trials.</li> <li><strong>lower_left_no_of_people</strong>: The number of individuals who passed through the bottleneck at the standardized start time.</li> <li><strong>right_shifted_time</strong>: Standardized end time for egress.</li> <li><strong>right_left_no_of_people</strong>: The number of individuals who passed through the bottleneck by the standardized end time.</li> </ul>
Supplementary material for "Language and User Requirements for Business Process Simulation - A Systematic Literature Review"
<p>This is ment as a supplementary material for the Publication "Language and User Requirements for Business Process Simulation - A Systematic Literature Review". It contains all sources that were used for the creation of the requirements broken down for each one.</p>
Assessing the Maturity level of Wearable Sensors for Home Monitoring in Parkinson's Disease through Evidence Evaluation Levels (EEL) and User Experience: A Comprehensive Review
<p>Source files for the PRISMA diagram and Figure 1 of the comprehensive review: Assessing the Maturity level of Wearable Sensors for Home Monitoring in Parkinson’s Disease through Evidence Evaluation Levels (EEL) and User Experience</p>
Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence
<p>This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. dataset folder</strong></p> <ul> <li>dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews.</li> <li>readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.</li> </ul>
Empirical data for project: Mining user reviews of COVID contact-tracing apps
<p>Empirical data for project: Mining user reviews of COVID contact-tracing apps</p>
user English reviews of Beat Saber on Steam
<p>user English reviews of Beat Saber on Steam</p>
user reviews of OTA
<p>User reviews</p>
Retrospective Chart Review of First-time Opsumit® (Macitentan) Users in the United States
ClinicalTrials.gov study NCT03197688. IPD Sharing: Not stated. Countries: 2. Publications: 5.
Implicit aspect-based opinion mining and analysis of airline industry based on user generated reviews
<p>Mining opinions from reviews has been a field of ever-growing research. These include mining opinions on document level, sentence-level, and even aspect level of a review. While explicitly mentioned aspects in a review have been widely researched, very little work has been done in gathering opinions on aspects that are <em><strong>implied </strong></em>and not explicitly mentioned. E.g. “<strong><em>the flight was spacious and there was plenty of legroom</em></strong>”. This gives an opinion on the <em><strong>entities </strong></em>of the <em><strong>cabin </strong></em>and <em><strong>seat </strong></em>of an airline. Words like “<strong><em>spacious</em></strong>” and phrases like “<strong><em>plenty of legroom</em></strong>” help identify these <em><strong>implied entities</strong></em> and the <strong><em>opinions </em></strong>attached to them. Not much research has been done for gathering such implicit aspects and opinions for airline reviews. The present dataset is a <em><strong>manually annotated domain-specific aspect-based corpus </strong></em>that helps a study to extract and analyze opinions about such implied aspects and entities of airlines.</p>
A scoping review exploring vocational rehabilitation interventions for mental health service users with chronic mental illness in low-income to upper-middle-income countries
<p class="MsoNormal"><strong><span>Objective</span></strong></p> <p class="MsoNormal"><span>To synthesize research published on vocational rehabilitation (VR) interventions offered in institutions, by occupational therapists, to mental health service users (MHSUs) with chronic mental illness, in low-income to upper middle-income countries (L-UMIC).</span></p> <p class="MsoNormal"><strong><span>Design</span></strong></p> <p class="MsoNormal"><span>This scoping review used Arksey and O'Malley's methodological framework, the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs scoping review guidelines. </span></p> <p class="MsoNormal"><strong><span>Data Sources</span></strong></p> <p class="MsoNormal"><span>We searched PsycInfo, EBSCOhost, HINARI, Google scholar, Medline, CINAHL, PubMed, Cochrane Library, Scopus, Science Direct and Wiley online library between 15 July and 31 August 2021. </span></p> <p class="MsoNormal"><strong><span>Eligibility Criteria</span></strong></p> <p class="MsoNormal"><span>Sources, published in English between 2011 and 2021, on institution-based VR in occupational therapy for MHSUs who had chronic mental illness in L-UMIC were included. We included primary studies of any design.</span></p> <p class="MsoNormal"><strong><span>Data extraction and synthesis</span></strong></p> <p class="MsoNormal"><span>Three reviewers used Mendeley to manage identified references, Rayyan for abstract and full text screening, and Microsoft Excel for data extraction. Data was sifted and sorted by key categories and themes.</span></p> <p class="MsoNormal"><strong><span>Results</span></strong></p> <p class="MsoNormal"><span>895 sources were identified, and their title and abstracts reviewed. 207 sources were included for full text screening. 12 articles from 4 countries (South Africa, India, Brazil & Kenya) were finally included. Types of VR intervention included supported employment, case management and prevocational </span><span>skills training. Client centeredness, support and empowerment were the key VR principles identified. Teaching of illness self-management, job analysis and matching, job coaching, trial placement, and vocational guidance and counseling, were the main intervention strategies reported.</span></p> <p class="MsoNormal"><strong><span>Conclusions </span></strong></p> <p class="MsoNormal"><span>VR intervention in institutions for MHSUs in L-UMIC revealed the multidimensional uniqueness of individual MHSU's vocational ability, needs and contexts. The interventions allowed client-centered approaches that offer support, and empowerment beyond the boundaries of the institutions. Occupational therapists offering VR need to expand their interventions beyond their institutions to contexts where MHSUs are working or intending to work.</span></p>
A scoping review exploring vocational rehabilitation interventions for mental health service users with chronic mental illness in low-income to upper-middle-income countries
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Body of Literature pertaining to the article "Investigating Trends and Current Practices of User Training in ERP Implementations: A Systematic Literature Review"
<p>This section contains the 40 articles that have been analysed in the structured literature review in the article "Investigating Trends and Current Practices of User Training in ERP Implementations: A Systematic Literature Review"</p>
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