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Opinion Mining for Software Development: A Systematic Literature Review
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/asi.21416openalex-snapshot
Sentiment strength detection in short informal text
inferredcites →https://doi.org/10.1002/asi.24067openalex-snapshot
To Do or Not To Do: Distill crowdsourced negative caveats to augment api documentation
inferredcites →https://doi.org/10.1002/cpe.5358openalex-snapshot
CROSA: Context‐aware cloud service ranking approach using online reviews based on sentiment analysis
inferredcites →https://doi.org/10.1002/smr.2112openalex-snapshot
Analyzing reviews guided by App descriptions for the software development and evolution
inferredcites →https://doi.org/10.1002/smr.2199openalex-snapshot
A systematic mapping study on crowdsourced requirements engineering using user feedback
inferredcites →https://doi.org/10.1007/978-1-4614-3223-4_13openalex-snapshot
A Survey of Opinion Mining and Sentiment Analysis
inferredcites →https://doi.org/10.1007/978-3-030-15538-4_4openalex-snapshot
RE-SWOT: From User Feedback to Requirements via Competitor Analysis
inferredcites →https://doi.org/10.1007/978-3-030-29374-1_32openalex-snapshot
Winning of Hearts and Minds: Integrating Sentiment Analytics into the Analysis of Contradictions
inferredcites →https://doi.org/10.1007/978-3-030-29551-6_71openalex-snapshot
A Multi-label Active Learning Approach for Mobile App User Review Classification
inferredcites →https://doi.org/10.1007/978-3-030-37858-5_38openalex-snapshot
Analysis of Newcomers Activity in Communicative Posts on GitHub
inferredcites →https://doi.org/10.1007/978-3-319-06605-9_48openalex-snapshot
For User-Driven Software Evolution: Requirements Elicitation Derived from Mining Online Reviews
inferredcites →https://doi.org/10.1007/978-3-319-26285-7_19openalex-snapshot
App Reviews: Breaking the User and Developer Language Barrier
inferredcites →https://doi.org/10.1007/978-3-319-33515-5_12openalex-snapshot
Arsonists or Firefighters? Affectiveness in Agile Software Development
inferredcites →https://doi.org/10.1007/978-3-319-59288-6_28openalex-snapshot
An Approach of Extracting Feature Requests from App Reviews
inferredcites →https://doi.org/10.1007/978-3-319-62105-0_5openalex-snapshot
Natural Language Insights from Code Reviews that Missed a Vulnerability
inferredcites →https://doi.org/10.1007/978-3-319-69926-4_17openalex-snapshot
A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models
inferredcites →https://doi.org/10.1007/978-3-319-91800-6_13openalex-snapshot
ALDA: An Aggregated LDA for Polarity Enhanced Aspect Identification Technique in Mobile App Domain
inferredcites →https://doi.org/10.1007/978-3-642-19460-3_11openalex-snapshot
Opinion Mining and Sentiment Analysis
inferredcites →https://doi.org/10.1007/978-3-642-20161-5_34openalex-snapshot
Comparing Twitter and Traditional Media Using Topic Models
inferredcites →https://doi.org/10.1007/978-3-642-29044-2openalex-snapshot
Experimentation in Software Engineering
inferredcites →https://doi.org/10.1007/978-3-642-38977-1_14openalex-snapshot
Assessing Software Quality through Web Comment Search and Analysis
inferredcites →https://doi.org/10.1007/978-3-642-41278-3_24openalex-snapshot
Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model
inferredcites →https://doi.org/10.1007/s00766-016-0251-9openalex-snapshot
On the automatic classification of app reviews
inferredcites →https://doi.org/10.1007/s00766-017-0274-xopenalex-snapshot
An exploratory study of Twitter messages about software applications
inferredcites →https://doi.org/10.1007/s10462-017-9599-6openalex-snapshot
A survey on classification techniques for opinion mining and sentiment analysis
inferredcites →https://doi.org/10.1007/s10664-015-9375-7openalex-snapshot
Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews
inferredcites →https://doi.org/10.1007/s10664-016-9493-xopenalex-snapshot
On negative results when using sentiment analysis tools for software engineering research
inferredcites →https://doi.org/10.1007/s10664-017-9526-0openalex-snapshot
An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems
inferredcites →https://doi.org/10.1007/s10664-017-9546-9openalex-snapshot
Sentiment Polarity Detection for Software Development
inferredcites →https://doi.org/10.1007/s10664-018-9601-1openalex-snapshot
App store mining is not enough for app improvement
inferredcites →https://doi.org/10.1007/s10664-018-9605-xopenalex-snapshot
Using frame semantics for classifying and summarizing application store reviews
inferredcites →https://doi.org/10.1007/s10664-018-9617-6openalex-snapshot
Studying the consistency of star ratings and reviews of popular free hybrid Android and iOS apps
inferredcites →https://doi.org/10.1007/s10664-019-09716-7openalex-snapshot
Mining non-functional requirements from App store reviews
inferredcites →https://doi.org/10.1007/s10664-019-09743-4openalex-snapshot
CAPS: a supervised technique for classifying Stack Overflow posts concerning API issues
inferredcites →https://doi.org/10.1007/s10664-021-09960-wopenalex-snapshot
Assessment of off-the-shelf SE-specific sentiment analysis tools: An extended replication study
inferredcites →https://doi.org/10.1007/s11042-018-6445-zopenalex-snapshot
A survey on sentiment analysis and opinion mining for social multimedia
inferredcites →https://doi.org/10.1007/s11334-017-0294-1openalex-snapshot
Automated classification of software issue reports using machine learning techniques: an empirical study
inferredcites →https://doi.org/10.1007/s42979-020-00149-4openalex-snapshot
A Survey on Challenges and Research Opportunities in Opinion Mining
inferredcites →https://doi.org/10.1016/j.cose.2019.02.010openalex-snapshot
Revealing the unrevealed: Mining smartphone users privacy perception on app markets
inferredcites →https://doi.org/10.1016/j.cosrev.2017.10.002openalex-snapshot
The evolution of sentiment analysis—A review of research topics, venues, and top cited papers
inferredcites →https://doi.org/10.1016/j.eswa.2018.05.037openalex-snapshot
Extracting useful software development information from mobile application reviews: A survey of intelligent mining techniques and tools
inferredcites →https://doi.org/10.1016/j.infsof.2013.03.008openalex-snapshot
The state of the art in automated requirements elicitation
inferredcites →https://doi.org/10.1016/j.infsof.2017.12.005openalex-snapshot
Exploring the links between software development task type, team attitudes and task completion performance: Insights from the Jazz repository
inferredcites →https://doi.org/10.1016/j.infsof.2019.06.005openalex-snapshot
An empirical study of sentiments in code reviews
inferredcites →https://doi.org/10.1016/j.infsof.2019.08.002openalex-snapshot
Taking the emotional pulse of software engineering — A systematic literature review of empirical studies
inferredcites →https://doi.org/10.1016/j.infsof.2020.106277openalex-snapshot
Mining API usage scenarios from stack overflow
inferredcites →https://doi.org/10.1016/j.ipm.2016.06.009openalex-snapshot
TensiStrength: Stress and relaxation magnitude detection for social media texts
inferredcites →https://doi.org/10.1016/j.ipm.2016.12.002openalex-snapshot
Opinion mining from online hotel reviews – A text summarization approach
inferredcites →https://doi.org/10.1016/j.is.2018.08.003openalex-snapshot
Speech-acts based analysis for requirements discovery from online discussions
inferredcites →https://doi.org/10.1016/j.jksuci.2018.04.012openalex-snapshot
A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews
inferredcites →https://doi.org/10.1016/j.joi.2017.06.005openalex-snapshot
Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the Literature
inferredcites →https://doi.org/10.1016/j.jss.2015.02.055openalex-snapshot
Sentiment Analysis in monitoring software development processes: An exploratory case study on GitHub's project issues
inferredcites →https://doi.org/10.1016/j.jss.2016.11.027openalex-snapshot
A systematic literature review: Opinion mining studies from mobile app store user reviews
inferredcites →https://doi.org/10.1016/j.jss.2017.12.028openalex-snapshot
Automatically classifying user requests in crowdsourcing requirements engineering
inferredcites →https://doi.org/10.1016/j.jss.2018.08.030openalex-snapshot
SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text
inferredcites →https://doi.org/10.1016/j.knosys.2015.06.015openalex-snapshot
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
inferredcites →https://doi.org/10.1016/j.knosys.2020.105633openalex-snapshot
Classifying emotions in Stack Overflow and JIRA using a multi-label approach
inferredcites →https://doi.org/10.1016/j.procs.2019.11.160openalex-snapshot
Towards Auto-labelling Issue Reports for Pull-Based Software Development using Text Mining Approach
inferredcites →https://doi.org/10.1049/iet-sen.2018.5192openalex-snapshot
Combining goal model with reviews for supporting the evolution of apps
inferredcites →https://doi.org/10.1108/ecam-02-2019-0097openalex-snapshot
A domain knowledge incorporated text mining approach for capturing user needs on BIM applications
inferredcites →https://doi.org/10.1109/access.2019.2891543openalex-snapshot
Information Recommendation Based on Domain Knowledge in App Descriptions for Improving the Quality of Requirements
inferredcites →https://doi.org/10.1109/access.2021.3067000openalex-snapshot
Extracting Arguments Based on User Decisions in App Reviews
inferredcites →https://doi.org/10.1109/aciiw.2017.8272591openalex-snapshot
EmoTxt: A toolkit for emotion recognition from text
inferredcites →https://doi.org/10.1109/affectre.2018.00006openalex-snapshot
How Angry are Your Customers? Sentiment Analysis of Support Tickets that Escalate
inferredcites →https://doi.org/10.1109/affectre.2018.00010openalex-snapshot
Usability Related Qualities Through Sentiment Analysis
inferredcites →https://doi.org/10.1109/aina.2016.148openalex-snapshot
Sentiment Analysis and Classification for Software as a Service Reviews
inferredcites →https://doi.org/10.1109/aire.2018.00007openalex-snapshot
ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information
inferredcites →https://doi.org/10.1109/apsec.2017.79openalex-snapshot
How Do Code Refactoring Activities Impact Software Developers' Sentiments? - An Empirical Investigation Into GitHub Commits
inferredcites →https://doi.org/10.1109/apsec.2018.00014openalex-snapshot
The OIRE Method - Overview and Initial Validation
inferredcites →https://doi.org/10.1109/apsec48747.2019.00060openalex-snapshot
Automatic Identification of Assumptions from the Hibernate Developer Mailing List
inferredcites →https://doi.org/10.1109/apsec48747.2019.00075openalex-snapshot
Understanding the Effect of Developer Sentiment on Fix-Inducing Changes: An Exploratory Study on GitHub Pull Requests
inferredcites →https://doi.org/10.1109/apsecw.2017.13openalex-snapshot
Sentiments Analysis in GitHub Repositories: An Empirical Study
inferredcites →https://doi.org/10.1109/ase.2015.101openalex-snapshot
Tool Support for Analyzing Mobile App Reviews
inferredcites →https://doi.org/10.1109/ase.2015.12openalex-snapshot
Development Emails Content Analyzer: Intention Mining in Developer Discussions (T)
inferredcites →https://doi.org/10.1109/ase.2015.57openalex-snapshot
"What Parts of Your Apps are Loved by Users?" (T)
inferredcites →https://doi.org/10.1109/ase.2015.88openalex-snapshot
Ensemble Methods for App Review Classification: An Approach for Software Evolution (N)
inferredcites →https://doi.org/10.1109/ase.2017.8115623openalex-snapshot
SentiCR: A customized sentiment analysis tool for code review interactions
inferredcites →https://doi.org/10.1109/ase.2017.8115656openalex-snapshot
Understanding feature requests by leveraging fuzzy method and linguistic analysis
inferredcites →https://doi.org/10.1109/ase.2019.00031openalex-snapshot
Emotions Extracted from Text vs. True Emotions–An Empirical Evaluation in SE Context
inferredcites →https://doi.org/10.1109/asew.2015.10openalex-snapshot
Automatically Mining Negative Code Examples from Software Developer Q & A Forums
inferredcites →https://doi.org/10.1109/aswec.2018.00013openalex-snapshot
Semi-Automated Extraction of New Requirements from Online Reviews for Software Product Evolution
inferredcites →https://doi.org/10.1109/cgc.2013.71openalex-snapshot
The Role of Emotions in Contributors Activity: A Case Study on the GENTOO Community
inferredcites →https://doi.org/10.1109/cic.2017.00014openalex-snapshot
Sentiment Identification for Collaborative, Geographically Dispersed, Cross-Functional Software Development Teams
inferredcites →https://doi.org/10.1109/comcom.2017.8167099openalex-snapshot
Quality measurement of android messaging application based on user experience in Microblog
inferredcites →https://doi.org/10.1109/compsac.2016.183openalex-snapshot
SatiIndicator: Leveraging User Reviews to Evaluate User Satisfaction of SourceForge Projects
inferredcites →https://doi.org/10.1109/cts.2012.6261099openalex-snapshot
Using sentiment analysis to measure the effects of leaders in global software development
inferredcites →https://doi.org/10.1109/dasc/picom/cbdcom/cyberscitech.2019.00185openalex-snapshot
Evaluating the Performance of Machine Learning Sentiment Analysis Algorithms in Software Engineering
inferredcites →https://doi.org/10.1109/ecticon.2013.6559593openalex-snapshot
Analyzing software reviews for software quality-based ranking
inferredcites →https://doi.org/10.1109/esem.2015.7321214openalex-snapshot
Retrieving Diverse Opinions from App Reviews
inferredcites →https://doi.org/10.1109/esem.2017.67openalex-snapshot
A Comparison of Dictionary Building Methods for Sentiment Analysis in Software Engineering Text
inferredcites →https://doi.org/10.1109/hicss.2016.465openalex-snapshot
Developer Behavior and Sentiment from Data Mining Open Source Repositories
inferredcites →https://doi.org/10.1109/icdmw.2017.61openalex-snapshot
Extracting User-Reported Mobile Application Defects from Online Reviews
inferredcites →https://doi.org/10.1109/iceca.2018.8474619openalex-snapshot
Emotion Mining and Sentiment Analysis in Software Engineering Domain
inferredcites →https://doi.org/10.1109/icgse.2009.33openalex-snapshot
Linguistic Challenges in Global Software Development: Lessons Learned in an International SW Development Division
inferredcites →https://doi.org/10.1109/icpc.2013.6613842openalex-snapshot
Extracting problematic API features from forum discussions
inferredcites →https://doi.org/10.1109/icse-nier.2017.18openalex-snapshot
Anger and Its Direction in Collaborative Software Development
inferredcites →https://doi.org/10.1109/icse-nier.2019.00016openalex-snapshot
Leveraging Small Software Engineering Data Sets with Pre-Trained Neural Networks
inferredcites →https://doi.org/10.1109/icse-seip.2019.00040openalex-snapshot
Emerging App Issue Identification from User Feedback: Experience on WeChat
inferredcites →https://doi.org/10.1109/icse-seis.2017.8openalex-snapshot
Crowdsourced Exploration of Mobile App Features: A Case Study of the Fort McMurray Wildfire
inferredcites →https://doi.org/10.1109/icse.2013.6606604openalex-snapshot
Analysis of user comments: An approach for software requirements evolution
inferredcites →https://doi.org/10.1109/icse.2017.53openalex-snapshot
Challenges for Static Analysis of Java Reflection - Literature Review and Empirical Study
inferredcites →https://doi.org/10.1109/icse.2019.00057openalex-snapshot
Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge
inferredcites →https://doi.org/10.1109/icse.2019.00066openalex-snapshot
Pattern-Based Mining of Opinions in Q&A Websites
inferredcites →https://doi.org/10.1109/icse43902.2021.00115openalex-snapshot
Automatic Extraction of Opinion-Based Q&A from Online Developer Chats
inferredcites →https://doi.org/10.1109/icsm.2015.7332474openalex-snapshot
How can i improve my app? Classifying user reviews for software maintenance and evolution
inferredcites →https://doi.org/10.1109/icsm.2015.7332491openalex-snapshot
Is this code written in English? A study of the natural language of comments and identifiers in practice
inferredcites →https://doi.org/10.1109/icsme.2017.40openalex-snapshot
Confusion Detection in Code Reviews
inferredcites →https://doi.org/10.1109/icsme.2019.00038openalex-snapshot
EmoD: An End-to-End Approach for Investigating Emotion Dynamics in Software Development
inferredcites →https://doi.org/10.1109/icsme.2019.00070openalex-snapshot
Ticket Tagger: Machine Learning Driven Issue Classification
inferredcites →https://doi.org/10.1109/icsme46990.2020.00017openalex-snapshot
Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?
inferredcites →https://doi.org/10.1109/icsme46990.2020.00025openalex-snapshot
Achieving Reliable Sentiment Analysis in the Software Engineering Domain using BERT
inferredcites →https://doi.org/10.1109/icss53362.2021.00026openalex-snapshot
BERT for Sentiment Classification in Software Engineering
inferredcites →https://doi.org/10.1109/icts.2019.8850962openalex-snapshot
Classification of Mobile Application User Reviews for Generating Tickets on Issue Tracking System
inferredcites →https://doi.org/10.1109/ieeestd.2017.8100771openalex-snapshot
ISO/IEC/IEEE International Standard - Systems and software engineering -- Software life cycle processes
inferredcites →https://doi.org/10.1109/innovate-data.2018.00010openalex-snapshot
A Cognitive Agent for Mining Bugs Reports, Feature Suggestions and Sentiment in a Mobile Application Store
inferredcites →https://doi.org/10.1109/jcsse.2013.6567355openalex-snapshot
Evaluating software quality in use using user reviews mining
inferredcites →https://doi.org/10.1109/mobilesoft.2017.3openalex-snapshot
Same App, Different App Stores: A Comparative Study
inferredcites →https://doi.org/10.1109/ms.2019.2919573openalex-snapshot
Sentiment Classification Using N-Gram Inverse Document Frequency and Automated Machine Learning
inferredcites →https://doi.org/10.1109/ms.2019.2922949openalex-snapshot
Comparing the Communication Tone and Responses of Users and Developers in Two R Mailing Lists: Measuring Positive and Negative Emails
inferredcites →https://doi.org/10.1109/ms.2019.2923408openalex-snapshot
Can a Machine Learn Through Customer Sentiment?: A Cost-Aware Approach to Predict Support Ticket Escalations
inferredcites →https://doi.org/10.1109/ms.2019.2924013openalex-snapshot
Sentiment and Emotion in Software Engineering
inferredcites →https://doi.org/10.1109/msr.2013.6624001openalex-snapshot
Retrieving and analyzing mobile apps feature requests from online reviews
inferredcites →https://doi.org/10.1109/msr.2015.35openalex-snapshot
Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time
inferredcites →https://doi.org/10.1109/msr.2017.27openalex-snapshot
Sentiment Analysis of Travis CI Builds
inferredcites →https://doi.org/10.1109/msr.2017.43openalex-snapshot
Rationale in Development Chat Messages: An Exploratory Study
inferredcites →https://doi.org/10.1109/msr.2017.47openalex-snapshot
Bootstrapping a Lexicon for Emotional Arousal in Software Engineering
inferredcites →https://doi.org/10.1109/msr.2019.00020openalex-snapshot
Exploring Word Embedding Techniques to Improve Sentiment Analysis of Software Engineering Texts
inferredcites →https://doi.org/10.1109/msr.2019.00058openalex-snapshot
Extracting API Tips from Developer Question and Answer Websites
inferredcites →https://doi.org/10.1109/naturalise.2013.6611717openalex-snapshot
Automatically identifying a software product's quality attributes through sentiment analysis of tweets
inferredcites →https://doi.org/10.1109/re.2014.6912257openalex-snapshot
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
inferredcites →https://doi.org/10.1109/re.2017.14openalex-snapshot
Mining Twitter Feeds for Software User Requirements
inferredcites →https://doi.org/10.1109/re.2017.82openalex-snapshot
Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning
inferredcites →https://doi.org/10.1109/re.2017.86openalex-snapshot
Mining User Rationale from Software Reviews
inferredcites →https://doi.org/10.1109/re.2017.88openalex-snapshot
A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution
inferredcites →https://doi.org/10.1109/re.2018.00024openalex-snapshot
Mining Android App Descriptions for Permission Requirements Recommendation
inferredcites →https://doi.org/10.1109/re.2018.00026openalex-snapshot
App Review Analysis Via Active Learning: Reducing Supervision Effort without Compromising Classification Accuracy
inferredcites →https://doi.org/10.1109/re.2019.00018openalex-snapshot
Analysis of Requirements-Related Arguments in User Forums
inferredcites →https://doi.org/10.1109/rew.2017.58openalex-snapshot
Automatically Classifying Requirements from App Stores: A Preliminary Study
inferredcites →https://doi.org/10.1109/rew.2017.76openalex-snapshot
Which Feature is Unusable? Detecting Usability and User Experience Issues from User Reviews
inferredcites →https://doi.org/10.1109/rew.2019.00022openalex-snapshot
What Can the Sentiment of a Software Requirements Specification Document Tell Us?
inferredcites →https://doi.org/10.1109/rew.2019.00046openalex-snapshot
Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning
inferredcites →https://doi.org/10.1109/saner.2016.113openalex-snapshot
The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse
inferredcites →https://doi.org/10.1109/saner.2017.7884612openalex-snapshot
Analyzing reviews and code of mobile apps for better release planning
inferredcites →https://doi.org/10.1109/saner.2018.8330245openalex-snapshot
A comparison of software engineering domain specific sentiment analysis tools
inferredcites →https://doi.org/10.1109/saner.2019.8667987openalex-snapshot
Expressions of Sentiments during Code Reviews: Male vs. Female
inferredcites →https://doi.org/10.1109/scam.2015.7335404openalex-snapshot
Recommending insightful comments for source code using crowdsourced knowledge
inferredcites →https://doi.org/10.1109/semotion.2017.1openalex-snapshot
Analyzing, Classifying, and Interpreting Emotions in Software Users' Tweets
inferredcites →https://doi.org/10.1109/semotion.2017.6openalex-snapshot
On the Emotion of Users in App Reviews
inferredcites →https://doi.org/10.1109/semotion.2019.00011openalex-snapshot
A Longitudinal Study on the Maintainers' Sentiment of a Large Scale Open Source Ecosystem
inferredcites →https://doi.org/10.1109/semotion.2019.00013openalex-snapshot
Developers' Sentiment and Issue Reopening
inferredcites →https://doi.org/10.1109/semotion.2019.00017openalex-snapshot
Empirical Analysis of Affect of Merged Issues on GitHub
inferredcites →https://doi.org/10.1109/sera.2016.7516145openalex-snapshot
Towards understanding and exploiting developers' emotional variations in software engineering
inferredcites →https://doi.org/10.1109/serp4iot.2019.00013openalex-snapshot
Topics of Concern: Identifying User Issues in Reviews of IoT Apps and Devices
inferredcites →https://doi.org/10.1109/sitis.2016.97openalex-snapshot
Stratify Mobile App Reviews: E-LDA Model Based on Hot "Entity" Discovery
inferredcites →https://doi.org/10.1109/snams.2019.8931820openalex-snapshot
Automatic Classification of Apps Reviews for Requirement Engineering: Exploring the Customers Need from Healthcare Applications
inferredcites →https://doi.org/10.1109/snpd.2016.7515956openalex-snapshot
Evaluating quality-in-use of FLOSS through analyzing user reviews
inferredcites →https://doi.org/10.1109/tse.2016.2630689openalex-snapshot
A Survey of App Store Analysis for Software Engineering
inferredcites →https://doi.org/10.1109/tse.2017.2759112openalex-snapshot
Listening to the Crowd for the Release Planning of Mobile Apps
inferredcites →https://doi.org/10.1109/tse.2019.2893171openalex-snapshot
Too Many User-Reviews! What Should App Developers Look at First?
inferredcites →https://doi.org/10.1109/tse.2019.2900245openalex-snapshot
Automatic Mining of Opinions Expressed About APIs in Stack Overflow
inferredcites →https://doi.org/10.1109/vissoft.2013.6650529openalex-snapshot
Visualizing emotions in software development projects
inferredcites →https://doi.org/10.1109/vissoft.2014.33openalex-snapshot
FAVe: Visualizing User Feedback for Software Evolution
inferredcites →https://doi.org/10.1145/1276318.1276363openalex-snapshot
Opinion mining in legal blogs
inferredcites →https://doi.org/10.1145/2484028.2484116openalex-snapshot
Sentiment analysis of user comments for one-class collaborative filtering over ted talks
inferredcites →https://doi.org/10.1145/2491411.2494578openalex-snapshot
Towards emotional awareness in software development teams
inferredcites →https://doi.org/10.1145/2568225.2568263openalex-snapshot
AR-miner: mining informative reviews for developers from mobile app marketplace
inferredcites →https://doi.org/10.1145/2597073.2597086openalex-snapshot
Do developers feel emotions? an exploratory analysis of emotions in software artifacts
inferredcites →https://doi.org/10.1145/2597073.2597117openalex-snapshot
Security and emotion: sentiment analysis of security discussions on GitHub
inferredcites →https://doi.org/10.1145/2597073.2597118openalex-snapshot
Sentiment analysis of commit comments in GitHub: an empirical study
inferredcites →https://doi.org/10.1145/2804381.2804387openalex-snapshot
The challenges of sentiment detection in the social programmer ecosystem
inferredcites →https://doi.org/10.1145/2884781.2884818openalex-snapshot
Release planning of mobile apps based on user reviews
inferredcites →https://doi.org/10.1145/2901739.2901752openalex-snapshot
Mining valence, arousal, and dominance
inferredcites →https://doi.org/10.1145/2901739.2901781openalex-snapshot
Sentiment analysis in tickets for IT support
inferredcites →https://doi.org/10.1145/2901739.2903501openalex-snapshot
Analyzing developer sentiment in commit logs
inferredcites →https://doi.org/10.1145/2901739.2903505openalex-snapshot
The emotional side of software developers in JIRA
inferredcites →https://doi.org/10.1145/2915970.2916003openalex-snapshot
Approaches for prioritizing feature improvements extracted from app reviews
inferredcites →https://doi.org/10.1145/2950290.2950299openalex-snapshot
What would users change in my app? summarizing app reviews for recommending software changes
inferredcites →https://doi.org/10.1145/2950290.2983938openalex-snapshot
ARdoc: app reviews development oriented classifier
inferredcites →https://doi.org/10.1145/2993259.2993267openalex-snapshot
Feature-based evaluation of competing apps
inferredcites →https://doi.org/10.1145/2993259.2993268openalex-snapshot
The impact of cross-platform development approaches for mobile applications from the user's perspective
inferredcites →https://doi.org/10.1145/3084226.3084241openalex-snapshot
Automatic Classification of Non-Functional Requirements from Augmented App User Reviews
inferredcites →https://doi.org/10.1145/3084226.3084246openalex-snapshot
Attributes that Predict which Features to Fix
inferredcites →https://doi.org/10.1145/3178876.3186168openalex-snapshot
A Feature-Oriented Sentiment Rating for Mobile App Reviews
inferredcites →https://doi.org/10.1145/3180155.3180195openalex-snapshot
Sentiment analysis for software engineering
inferredcites →https://doi.org/10.1145/3194932.3194934openalex-snapshot
The evolution of emotional displays in open source software development teams
inferredcites →https://doi.org/10.1145/3194932.3194935openalex-snapshot
Entity-level sentiment analysis of issue comments
inferredcites →https://doi.org/10.1145/3194932.3194936openalex-snapshot
On measuring affects of github issues' commenters
inferredcites →https://doi.org/10.1145/3194932.3194938openalex-snapshot
Sentiment and politeness analysis tools on developer discussions are unreliable, but so are people
inferredcites →https://doi.org/10.1145/3196398.3196403openalex-snapshot
A benchmark study on sentiment analysis for software engineering research
inferredcites →https://doi.org/10.1145/3196398.3196453openalex-snapshot
A gold standard for emotion annotation in stack overflow
inferredcites →https://doi.org/10.1145/3236024.3264595openalex-snapshot
INFAR: insight extraction from app reviews
inferredcites →https://doi.org/10.1145/3238147.3238208openalex-snapshot
Tell them apart: distilling technology differences from crowd-scale comparison discussions
inferredcites →https://doi.org/10.1145/3239235.3267434openalex-snapshot
On the use of emoticons in open source software development
inferredcites →https://doi.org/10.1145/3273934.3273943openalex-snapshot
Mining Communication Patterns in Software Development
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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