Bin Lin
Alexander Serebrenik
Michele Lanza
Skip Abstract Section
Abstract
Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail.
We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.
- [1] ACM Digital Library. Retrieved from https://dl.acm.org/.Google Scholar
- [2] Aylien. Retrieved from https://aylien.com.Google Scholar
- [3] Elsevier ScienceDirect. Retrieved from https://www.sciencedirect.com/.Google Scholar
- [4] IEEE Xplore Digital Library. Retrieved from https://ieeexplore.ieee.org/.Google Scholar
- [5] Introduction to the Syuzhet Package. Retrieved from https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html.Google Scholar
- [6] LIWC2015. Retrieved from https://liwc.wpengine.com.Google Scholar
- [7] Microsoft Azure Text Analytics. Retrieved from https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/.Google Scholar
- [8] Rosette Sentiment Analyzer. Retrieved from https://www.rosette.com/capability/sentiment-analyzer/.Google Scholar
- [9] Scopus. Retrieved from https://www.scopus.com/.Google Scholar
- [10] SentiSE. Retrieved from https://github.com/amiangshu/SentiSE.Google Scholar
- [11] Springer Link Online Library. Retrieved from https://link.springer.com/.Google Scholar
- [12] TextBlob: Simplified Text Processing. Retrieved from https://textblob.readthedocs.io/.Google Scholar
- [13] Watson Natural Language Understanding. Retrieved from https://www.ibm.com/cloud/watson-natural-language-understanding.Google Scholar
- [14] Wiley Online Library. Retrieved from https://onlinelibrary.wiley.com/.Google Scholar
- [15] 2017. ISO/IEC/IEEE International Standard - systems and software engineering – software life cycle processes. ISO/IEC/IEEE 12207:2017(E) First edition 2017-11 (2017). IEEE, 1–157.
DOI: Google ScholarCross Ref
- [16] . 2018. ELICA: An automated tool for dynamic extraction of requirements relevant information. In Proceedings of the 5th International Workshop on Artificial Intelligence for Requirements Engineering, , , , and (Eds.). IEEE, 8–14.
DOI: Google ScholarCross Ref - [17] . 2019. Supporting analysts by dynamic extraction and classification of requirements-related knowledge. In Proceedings of the 41st International Conference on Software Engineering, , , and (Eds.). IEEE/ACM, 442–453.
DOI: Google ScholarDigital Library
- [18] . 2020. CAPS: A supervised technique for classifying stack overflow posts concerning API issues. Empir. Softw. Eng. 25, 2 (2020), 1493–1532.
DOI: Google ScholarCross Ref - [19] . 2017. SentiCR: A customized sentiment analysis tool for code review interactions. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 106–111.
DOI: Google ScholarCross Ref - [20] . 2019. Sentiment analysis of open source software community mailing list: A preliminary analysis. In Proceedings of the 15th International Symposium on Open Collaboration, , , , and (Eds.). ACM, 21:1–21:5.
DOI: Google ScholarDigital Library - [21] . 2017. Same app, different app stores: A comparative study. In Proceedings of the 4th IEEE/ACM International Conference on Mobile Software Engineering and Systems. IEEE, 79–90.
DOI: Google ScholarDigital Library - [22] . 2019. Value-oriented requirements: Eliciting domain requirements from social network services to evolve software product lines. Appl. Sci. 9, 19 (2019), 3944.Google ScholarCross Ref
- [23] . 2019. Your opinions let us know: Mining social network sites to evolve software product lines. KSII Trans. Internet Inf. Syst. 13, 8 (2019), 4191–4211.
DOI: Google ScholarCross Ref - [24] . 2017. Rationale in development chat messages: An exploratory study. In Proceedings of the 14th International Conference on Mining Software Repositories, , , and (Eds.). IEEE Computer Society, 436–446.
DOI: Google ScholarDigital Library - [25] . 2016. Sentiment analysis and classification for software as a service reviews. In Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications, , , , , and (Eds.). IEEE Computer Society, 53–58.
DOI: Google ScholarCross Ref - [26] . 2017. Press one for POTUS, two for the German chancellor: Humor, race, and rematerialization in the Indian tech diaspora. HAU: Ethnogr. Theor. 7, 1 (2017), 327–352.
DOI: Google ScholarCross Ref - [27] . 2017. Quality measurement of Android messaging application based on user experience in microblog. In Proceedings of the International Conference on Applied Computer and Communication Technologies (ComCom). IEEE, 1–5.Google ScholarCross Ref
- [28] . 2019. An empirical study of sentiments in code reviews. Inf. Softw. Technol. 114 (2019), 37–54.
DOI: Google ScholarDigital Library - [29] . 2020. A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews. J. King Saud Univ. - Comput. Inf. Sci. 32, 1 (2020), 113–125.
DOI: Google ScholarDigital Library - [30] . 2017. Which feature is unusable? Detecting usability and user experience issues from user reviews. In Proceedings of the IEEE 25th International Requirements Engineering Conference Workshops. IEEE Computer Society, 182–187.
DOI: Google ScholarCross Ref - [31] . 2018. NTUA-SLP at SemEval-2018 Task 1: Predicting affective content in tweets with deep attentive RNNs and transfer learning. In Proceedings of the 12th International Workshop on Semantic Evaluation. Association for Computational Linguistics, 245–255.
DOI: Google ScholarCross Ref - [32] . 2021. CROSA: Context-aware cloud service ranking approach using online reviews based on sentiment analysis. Concurr. Computat.: Pract. Exper. 33, 7 (2021), e5358.
DOI: Google ScholarCross Ref - [33] . 2020. Achieving reliable sentiment analysis in the software engineering domain using BERT. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME). 162–173.
DOI: Google ScholarCross Ref - [34] . 2019. Exploring word embedding techniques to improve sentiment analysis of software engineering texts. In Proceedings of the 16th International Conference on Mining Software Repositories, , , and (Eds.). IEEE/ACM, 68–78.
DOI: Google ScholarDigital Library - [35] . 2016. Sentiment analysis in tickets for IT support. In Proceedings of the 13th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 235–246.
DOI: Google ScholarDigital Library - [36] . 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993–1022.Google ScholarCross Ref
- [37] . 2012. Using sentiment analysis to measure the effects of leaders in global software development. In Proceedings of the International Conference on Collaboration Technologies and Systems, and (Eds.). IEEE, 517–524.
DOI: Google ScholarCross Ref - [38] . 2018. Semi-automated extraction of new requirements from online reviews for software product evolution. In Proceedings of the 25th Australasian Software Engineering Conference. IEEE Computer Society, 31–40.
DOI: Google ScholarCross Ref - [39] . 2020. Classifying emotions in stack overflow and JIRA using a multi-label approach. Knowl.-based Syst. 195 (2020), 105633.
DOI: Google ScholarCross Ref - [40] . 2018. Sentiment polarity detection for software development. Empir. Softw. Eng. 23, 3 (2018), 1352–1382.
DOI: Google ScholarDigital Library - [41] . 2017. EmoTxt: A toolkit for emotion recognition from text. In Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (’17). IEEE Computer Society, 79–80.
DOI: Google ScholarCross Ref - [42] . 2013. Analysis of user comments: An approach for software requirements evolution. In Proceedings of the 35th International Conference on Software Engineering, , , and (Eds.). IEEE Computer Society, 582–591.
DOI: Google ScholarCross Ref - [43] . 2021. Automatic extraction of opinion-based Q&A from online developer chats. In Proceedings of the IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 1260–1272.Google ScholarDigital Library
- [44] . 2014. AR-miner: Mining informative reviews for developers from mobile app marketplace. In 36th International Conference on Software Engineering, , , and (Eds.). ACM, 767–778.
DOI: Google ScholarDigital Library - [45] . 2019. SEntiMoji: An emoji-powered learning approach for sentiment analysis in software engineering. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery, 841–852.
DOI: Google ScholarDigital Library - [46] . 2019. Developers’ sentiment and issue reopening. In Proceedings of the 4th International Workshop on Emotion Awareness in Software Engineering. IEEE/ACM, 29–33.
DOI: Google ScholarDigital Library - [47] . 2017. Analyzing reviews and code of mobile apps for better release planning. In Proceedings of the IEEE 24th International Conference on Software Analysis, Evolution and Reengineering, , , and (Eds.). IEEE Computer Society, 91–102.
DOI: Google ScholarCross Ref - [48] . 2018. On the use of emoticons in open source software development. In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. Association for Computing Machinery, 4 pages.
DOI: Google ScholarDigital Library - [49] . 2007. Opinion mining in legal blogs. In Proceedings of the 11th International Conference on Artificial Intelligence and Law (ICAIL’07). ACM, 231–236.
DOI: Google ScholarDigital Library - [50] . 2016. Estimating trust in virtual teams—A framework based on sentiment analysis. In Proceedings of the 18th International Conference on Enterprise Information Systems, , , , , and (Eds.). SciTePress, 464–471.
DOI: Google ScholarDigital Library - [51] . 2019. RE-SWOT: From user feedback to requirements via competitor analysis. In 25th International Working Conference on Requirements Engineering: Foundation for Software Quality(
Lecture Notes in Computer Science , Vol. 11412), and (Eds.). Springer, 55–70.DOI: Google ScholarCross Ref - [52] . 2013. A computational approach to politeness with application to social factors. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. The Association for Computer Linguistics, 250–259. Retrieved from https://www.aclweb.org/anthology/P13-1025/.Google Scholar
- [53] . 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International World Wide Web Conference (WWW’03). ACM, 519–528.
DOI: Google ScholarDigital Library - [54] . 2013. Automatically identifying a software product’s quality attributes through sentiment analysis of tweets. In Proceedings of the 1st International Workshop on Natural Language Analysis in Software Engineering (NaturaLiSE’13). 25–30.
DOI: Google ScholarCross Ref - [55] . 2013. Automatically identifying a software product’s quality attributes through sentiment analysis of tweets. In Proceedings of the 1st International Workshop on Natural Language Analysis in Software Engineering (NaturaLiSE). 25–30.
DOI: Google ScholarCross Ref - [56] . 2017. Automatically classifying requirements from app stores: A preliminary study. In Proceedings of the IEEE 25th International Requirements Engineering Conference Workshops. IEEE Computer Society, 367–371.
DOI: Google ScholarCross Ref - [57] . 2018. On measuring affects of GitHub issues’ commenters. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, , , and (Eds.). ACM, 14–19.
DOI: Google ScholarDigital Library - [58] . 2016. Software development: Do good manners matter? PeerJ Comput. Sci. 2 (2016), e73.
DOI: Google ScholarCross Ref - [59] . 2018. App review analysis via active learning: Reducing supervision effort without compromising classification accuracy. In Proceedings of the IEEE 26th International Requirements Engineering Conference (RE). 170–181.
DOI: Google ScholarCross Ref - [60] . 2018. Entity-level sentiment analysis of issue comments. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, , , and (Eds.). ACM, 7–13.
DOI: Google ScholarDigital Library - [61] . 2018. Please please me: Does the presence of test cases influence mobile app users’ satisfaction? In Proceedings of the 32nd Brazilian Symposium on Software Engineering, (Ed.). ACM, 132–141.
DOI: Google ScholarDigital Library - [62] . 2017. Confusion detection in code reviews. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution. IEEE Computer Society, 549–553.
DOI: Google ScholarCross Ref - [63] . 2014. Software usability evaluation using opinion mining. J. Softw. 9, 2 (2014), 343–349.
DOI: Google ScholarCross Ref - [64] . 2019. Towards auto-labelling issue reports for pull-based software development using text mining approach. Proced. Comput. Sci. 161 (2019), 585–592.
DOI: Google ScholarDigital Library - [65] . 2019. A longitudinal study on the maintainers’ sentiment of a large scale open source ecosystem. In Proceedings of the 4th International Workshop on Emotion Awareness in Software Engineering. IEEE/ACM, 17–22.
DOI: Google ScholarDigital Library - [66] . 2019. Winning of hearts and minds: Integrating sentiment analytics into the analysis of contradictions. In Proceedings of the 18th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society—Digital Transformation for a Sustainable Society in the 21st Century(
Lecture Notes in Computer Science , Vol. 11701), , , , , , and (Eds.). Springer, 392–403.DOI: Google ScholarDigital Library - [67] . 2019. Sentiment analysis of open source communities: An exploratory study. In Proceedings of the 15th International Symposium on Open Collaboration, , , , and (Eds.). ACM, 20:1–20:5.
DOI: Google ScholarDigital Library - [68] . 2013. Why people hate your app: Making sense of user feedback in a mobile app store. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , , , , , , , , and (Eds.). ACM, 1276–1284.
DOI: Google ScholarDigital Library - [69] . 2019. On using machine learning to identify knowledge in API reference documentation. In Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, , , , and (Eds.). ACM, 109–119.
DOI: Google ScholarDigital Library - [70] . 2017. Anger and its direction in collaborative software development. In Proceedings of the 39th IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track. IEEE Computer Society, 11–14.
DOI: Google ScholarDigital Library - [71] . 2018. INFAR: Insight extraction from app reviews. In Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, , , and (Eds.). ACM, 904–907.
DOI: Google ScholarDigital Library - [72] . 2019. Emerging app issue identification from user feedback: Experience on WeChat. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, and (Eds.). IEEE/ACM, 279–288.
DOI: Google ScholarDigital Library - [73] . 2013. The role of emotions in contributors activity: A case study on the GENTOO community. In Proceedings of the International Conference on Cloud and Green Computing. IEEE Computer Society, 410–417.
DOI: Google ScholarDigital Library - [74] . 2017. A systematic literature review: Opinion mining studies from mobile app store user reviews. J. Syst. Softw. 125 (2017), 207–219.Google ScholarCross Ref
- [75] . 2015. What parts of your apps are loved by users?. In Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 760–770.
DOI: Google ScholarDigital Library - [76] . 2013. Visualizing emotions in software development projects. In Proceedings of the 1st IEEE Working Conference on Software Visualization (VISSOFT), , , and (Eds.). IEEE Computer Society, 1–4.
DOI: Google ScholarCross Ref - [77] . 2017. An exploratory study of Twitter messages about software applications. Requir. Eng. 22, 3 (2017), 387–412.
DOI: Google ScholarDigital Library - [78] . 2015. Retrieving diverse opinions from app reviews. In Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, 21–30.
DOI: Google ScholarCross Ref - [79] . 2014. Sentiment analysis of commit comments in GitHub: An empirical study. In Proceedings of the 11th Working Conference on Mining Software Repositories, , , and (Eds.). ACM, 352–355.
DOI: Google ScholarDigital Library - [80] . 2014. FAVe: Visualizing user feedback for software evolution. In Proceedings of the 2nd IEEE Working Conference on Software Visualization, , , and (Eds.). IEEE Computer Society, 167–171.
DOI: Google ScholarDigital Library - [81] . 2013. Towards emotional awareness in software development teams. In Proceedings of the Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE’13). ACM, 671–674.
DOI: Google ScholarDigital Library - [82] . 2013. Towards emotional awareness in software development teams. In Proceedings of the Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE’13, , , and (Eds.). ACM, 671–674.
DOI: Google ScholarDigital Library - [83] . 2015. Ensemble methods for app review classification: An approach for software evolution (N). In Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 771–776.
DOI: Google ScholarDigital Library - [84] . 2017. A little bird told me: Mining tweets for requirements and software evolution. In Proceedings of the 25th IEEE International Requirements Engineering Conference, , , , and (Eds.). IEEE Computer Society, 11–20.
DOI: Google ScholarCross Ref - [85] . 2014. How do users like this feature? A fine grained sentiment analysis of app reviews. In Proceedings of the IEEE 22nd International Requirements Engineering Conference, and (Eds.). IEEE Computer Society, 153–162.
DOI: Google ScholarCross Ref - [86] . 2017. Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation: Review of the literature. J. Inform. 11, 3 (2017), 823–834.
DOI: Google ScholarCross Ref - [87] . 2019. Revealing the unrevealed: Mining smartphone users privacy perception on app markets. Comput. Secur. 83 (2019), 332–353.
DOI: Google ScholarDigital Library - [88] . 2019. A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev. 52, 3 (2019), 1495–1545.
DOI: Google ScholarDigital Library - [89] . 2016. App reviews: Breaking the user and developer language barrier. In Trends and Applications in Software Engineering, , , , and (Eds.). Springer International Publishing, Cham, 223–233.Google ScholarCross Ref
- [90] . 2019. Studying the consistency of star ratings and reviews of popular free hybrid Android and iOS apps. Empir. Softw. Eng. 24, 1 (2019), 7–32.
DOI: Google ScholarDigital Library - [91] . 2017. Opinion mining from online hotel reviews—A text summarization approach. Inf. Process. Manag. 53, 2 (2017), 436–449.
DOI: Google ScholarDigital Library - [92] . 2018. Tell them apart: Distilling technology differences from crowd-scale comparison discussions. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, , , and (Eds.). ACM, 214–224.
DOI: Google ScholarDigital Library - [93] . 2018. What people like in mobile finance apps: An analysis of user reviews. In Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia, and (Eds.). ACM, 293–304.
DOI: Google ScholarDigital Library - [94] . 2019. Understanding the effect of developer sentiment on fix-inducing changes: An exploratory study on GitHub pull requests. In Proceedings of the 26th Asia-Pacific Software Engineering Conference. IEEE, 514–521.
DOI: Google ScholarCross Ref - [95] . 2014. VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International Conference on Weblogs and Social Media (’14). The AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109.Google Scholar
- [96] . 2016. MARAM: Tool support for mobile app review management. In Proceedings of the 8th EAI International Conference on Mobile Computing, Applications and Services, , , and (Eds.). ACM/ICST, 42–50.
DOI: Google ScholarDigital Library - [97] . 2013. Retrieving and analyzing mobile apps feature requests from online reviews. In Proceedings of the 10th Working Conference on Mining Software Repositories (MSR’13). IEEE Computer Society, 41–44.
DOI: Google ScholarCross Ref - [98] . 2016. Open source software adoption evaluation through feature level sentiment analysis using Twitter data. Turk. J. Electric. Eng. Comput. Sci. 24, 5 (2016), 4481–4496.Google ScholarCross Ref
- [99] . 2018. Sentiment and politeness analysis tools on developer discussions are unreliable, but so are people. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, , , and (Eds.). ACM, 55–61.
DOI: Google ScholarDigital Library - [100] . 2019. MarValous: Machine learning based detection of emotions in the valence-arousal space in software engineering text. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, and (Eds.). ACM, 1786–1793.
DOI: Google ScholarDigital Library - [101] . 2016. Exploration and exploitation of developers’ sentimental variations in software engineering. Int. J. Softw. Innov. 4, 4 (2016), 35–55.
DOI: Google ScholarDigital Library - [102] . 2016. Towards understanding and exploiting developers’ emotional variations in software engineering. In Proceedings of the 14th IEEE International Conference on Software Engineering Research, Management and Applications, (Ed.). IEEE Computer Society, 185–192.
DOI: Google ScholarCross Ref - [103] . 2017. A comparison of dictionary building methods for sentiment analysis in software engineering text. In Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, , , and (Eds.). IEEE Computer Society, 478–479.
DOI: Google ScholarDigital Library - [104] . 2018. A comparison of software engineering domain specific sentiment analysis tools. In Proceedings of the 25th International Conference on Software Analysis, Evolution and Reengineering, , , and (Eds.). IEEE Computer Society, 487–491.
DOI: Google ScholarCross Ref - [105] . 2018. DEVA: Sensing emotions in the valence arousal space in software engineering text. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, , , and (Eds.). ACM, 1536–1543.
DOI: Google ScholarDigital Library - [106] . 2018. SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text. J. Syst. Softw. 145 (2018), 125–146.
DOI: Google ScholarCross Ref - [107] . 2013. Analyzing software reviews for software quality-based ranking. In Proceedings of the 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. 1–6.
DOI: Google ScholarCross Ref - [108] . 2017. MARC: A mobile application review classifier. In Joint Proceedings of REFSQ-2017 Workshops, Doctoral Symposium, Research Method Track, and Poster Track co-located with the 22nd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ’17)(
CEUR Workshop Proceedings , Vol. 1796), , , , , , , , , , , , , , , , , , , , , , and (Eds.). CEUR-WS.org. Retrieved from http://ceur-ws.org/Vol-1796/poster-paper-1.pdf.Google Scholar - [109] . 2018. Using frame semantics for classifying and summarizing application store reviews. Empir. Softw. Eng. 23, 6 (2018), 3734–3767.
DOI: Google ScholarDigital Library - [110] . 2019. Mining non-functional requirements from App store reviews. Empir. Softw. Eng. 24, 6 (2019), 3659–3695.
DOI: Google ScholarCross Ref - [111] . 2019. Recommending new features from mobile app descriptions. ACM Trans. Softw. Eng. Methodol. 28, 4 (2019), 22:1–22:29.
DOI: Google ScholarDigital Library - [112] . 2014. For user-driven software evolution: Requirements elicitation derived from mining online reviews. In Proceedings of the 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining(
Lecture Notes in Computer Science , Vol. 8444), , , , , and (Eds.). Springer, 584–595.DOI: Google ScholarCross Ref - [113] . 2017. On negative results when using sentiment analysis tools for software engineering research. Empir. Softw. Eng. 22, 5 (2017), 2543–2584.
DOI: .Google ScholarDigital Library - [114] . 2015. Sentiment Analysis in monitoring software development processes: An exploratory case study on GitHub’s project issues. J. Syst. Softw. 104 (2015), 82–89.
DOI: Google ScholarDigital Library - [115] . 2019. Ticket tagger: Machine learning driven issue classification. In Proceedings of the International Conference on Software Maintenance and Evolution. IEEE, 406–409.
DOI: Google ScholarCross Ref - [116] . 2018. Emotion mining and sentiment analysis in software engineering domain. In Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA). 1170–1173.
DOI: Google ScholarCross Ref - [117] . 2016. Approaches for prioritizing feature improvements extracted from app reviews. In Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, , , and (Eds.). ACM, 33:1–33:6.
DOI: Google ScholarDigital Library - [118] . 2019. Analysis of requirements-related arguments in user forums. In Proceedings of the 27th IEEE International Requirements Engineering Conference, , , and (Eds.). IEEE, 63–74.
DOI: Google ScholarCross Ref - [119] . 2019. Automatic classification of apps reviews for requirement engineering: Exploring the customers need from healthcare applications. In Proceedings of the 6th International Conference on Social Networks Analysis, Management and Security, and (Eds.). IEEE, 541–548.
DOI: Google ScholarCross Ref - [120] . 2007. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report. EBSE 2007-001. Keele University and Durham University Joint Report.Google Scholar
- [121] . 2020. A survey on challenges and research opportunities in opinion mining. SN Comput. Sci. 1, 3 (2020), 171.
DOI: Google ScholarCross Ref - [122] . 2021. Extracting arguments based on user decisions in app reviews. IEEE Access 9 (2021), 45078–45094.Google ScholarCross Ref
- [123] . 2018. ALDA: An aggregated LDA for polarity enhanced aspect identification technique in mobile app domain. In Proceedings of the 13th International Conference on Designing for a Digital and Globalized World(
Lecture Notes in Computer Science , Vol. 10844), , , and (Eds.). Springer, 187–204.DOI: Google ScholarCross Ref - [124] . 2017. Automatically classifying functional and non-functional requirements using supervised machine learning. In Proceedings of the 25th IEEE International Requirements Engineering Conference, , , , and (Eds.). IEEE Computer Society, 490–495.
DOI: Google ScholarCross Ref - [125] . 2017. Mining user rationale from software reviews. In Proceedings of the 25th IEEE International Requirements Engineering Conference, , , , and (Eds.). IEEE Computer Society, 61–70.
DOI: Google ScholarCross Ref - [126] . 2017. Challenges for static analysis of Java reflection: Literature review and empirical study. In Proceedings of the 39th International Conference on Software Engineering, , , and (Eds.). IEEE/ACM, 507–518.
DOI: Google ScholarDigital Library - [127] . 2019. Comparing the communication tone and responses of users and developers in two R mailing lists: Measuring positive and negative emails. IEEE Softw. 36, 5 (2019), 46–50.
DOI: Google ScholarDigital Library - [128] . 2013. Evaluating software quality in use using user reviews mining. In Proceedings of the 10th International Joint Conference on Computer Science and Software Engineering (JCSSE). 257–262.
DOI: Google ScholarCross Ref - [129] . 2018. Automatically classifying user requests in crowdsourcing requirements engineering. J. Syst. Softw. 138 (2018), 108–123.
DOI: Google ScholarCross Ref - [130] . 2018. To do or not to do: Distill crowdsourced negative caveats to augment API documentation. J. Assoc. Inf. Sci. Technol. 69, 12 (2018), 1460–1475.
DOI: Google ScholarDigital Library - [131] . 2019. Automatic identification of assumptions from the hibernate developer mailing list. In Proceedings of the 26th Asia-Pacific Software Engineering Conference. IEEE, 394–401.
DOI: Google ScholarCross Ref - [132] . 2019. A survey on sentiment analysis and opinion mining for social multimedia. Multim. Tools Appl. 78, 6 (2019), 6939–6967.
DOI: Google ScholarDigital Library - [133] . 2014. Relating IS developers’ attitudes to engagement. In Proceedings of the 25th Australasian Conference on Information Systems (ACIS’14). 1–10.Google Scholar
- [134] . 2018. Exploring the links between software development task type, team attitudes and task completion performance: Insights from the Jazz repository. Inf. Softw. Technol. 97 (2018), 10–25.
DOI: Google ScholarCross Ref - [135] . 2017. Attributes that predict which features to fix: Lessons for app store mining. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, , , and (Eds.). ACM, 108–117.
DOI: Google ScholarDigital Library - [136] . 2021. Replication Package for “Opinion Mining for Software Development: A Systematic Literature Review.”
DOI: Google ScholarCross Ref - [137] . 2019. Pattern-based mining of opinions in Q&A websites. In Proceedings of the 41st International Conference on Software Engineering, , , and (Eds.). IEEE/ACM, 548–559.
DOI: Google ScholarDigital Library - [138] . 2018. Sentiment analysis for software engineering: How far can we go? In Proceedings of the 40th International Conference on Software Engineering, , , , and (Eds.). ACM, 94–104.
DOI: Google ScholarDigital Library - [139] . 2018. Sentiment analysis for software engineering: How far can we go? In Proceedings of the 40th International Conference on Software Engineering, , , , and (Eds.). ACM, 94–104.
DOI: Google ScholarDigital Library - [140] . 2011. Opinion mining and sentiment analysis. In Web Data Mining. Springer, 459–526.Google ScholarCross Ref
- [141] . 2011. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Second Edition. Springer.
DOI: Google ScholarCross Ref - [142] . 2015. Sentiment Analysis - Mining Opinions, Sentiments, and Emotions. Cambridge University Press. Retrieved from http://www.cambridge.org/us/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/sentiment-analysis-mining-opinions-sentiments-and-emotions.Google ScholarCross Ref
- [143] . 2012. A survey of opinion mining and sentiment analysis. In Mining Text Data, and (Eds.). Springer, 415–463.
DOI: Google ScholarCross Ref - [144] . 2018. Mining android app descriptions for permission requirements recommendation. In Proceedings of the 26th IEEE International Requirements Engineering Conference, , , and (Eds.). IEEE Computer Society, 147–158.
DOI: Google ScholarCross Ref - [145] . 2016. Stratify mobile app reviews: E-LDA model based on hot “Entity” discovery. In Proceedings of the 12th International Conference on Signal-Image Technology & Internet-Based Systems, , , , , and (Eds.). IEEE Computer Society, 581–588.
DOI: Google ScholarCross Ref - [146] . 2020. Combining goal model with reviews for supporting the evolution of apps. IET Softw. 14, 1 (2020), 39–49.
DOI: Google ScholarDigital Library - [147] . 2019. Information recommendation based on domain knowledge in app descriptions for improving the quality of requirements. IEEE Access 7 (2019), 9501–9514.
DOI: Google ScholarCross Ref - [148] . 2018. Analyzing reviews guided by App descriptions for the software development and evolution. J. Softw. Evol. Process. 30, 12 (2018).
DOI: Google ScholarDigital Library - [149] . 2017. Automatic classification of non-functional requirements from augmented app user reviews. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, , , and (Eds.). ACM, 344–353.
DOI: Google ScholarDigital Library - [150] . 2018. A feature-oriented sentiment rating for mobile app reviews. In Proceedings of the World Wide Web Conference on World Wide Web, , , , and (Eds.). ACM, 1909–1918.
DOI: Google ScholarDigital Library - [151] . 2009. Linguistic challenges in global software development: Lessons learned in an international SW development division. In Proceedings of the 4th IEEE International Conference on Global Software Engineering. 249–253.
DOI: Google ScholarDigital Library - [152] . 2016. On the automatic classification of app reviews. Requir. Eng. 21, 3 (2016), 311–331.
DOI: Google ScholarDigital Library - [153] . 2019. Sentiment classification using N-Gram inverse document frequency and automated machine learning. IEEE Softw. 36, 5 (2019), 65–70.
DOI: Google ScholarDigital Library - [154] . 2016. Mining valence, arousal, and dominance: Possibilities for detecting burnout and productivity? In Proceedings of the 13th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 247–258.
DOI: Google ScholarDigital Library - [155] . 2018. The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27 (2018), 16–32.
DOI: Google ScholarCross Ref - [156] . 2017. Bootstrapping a lexicon for emotional arousal in software engineering. In IEEE/ACM 14th International Conference on Mining Software Repositories (MSR’17). 198–202.
DOI: Google ScholarDigital Library - [157] . 2017. On the emotion of users in app reviews. In Proceedings of the 2nd IEEE/ACM International Workshop on Emotion Awareness in Software Engineering. IEEE Computer Society, 8–14.
DOI: Google ScholarCross Ref - [158] . 2017. A survey of app store analysis for software engineering. IEEE Trans. Softw. Eng. 43, 9 (2017), 817–847.Google ScholarDigital Library
- [159] . 2016. Feature-based sentiment analysis of codified project knowledge: A dictionary approach. In Proceedings of the Pacific Asia Conference On Information Systems (PACIS). Association for Information System.Google Scholar
- [160] . 2016. Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews. Empir. Softw. Eng. 21, 3 (2016), 1067–1106.
DOI: Google ScholarDigital Library - [161] . 2016. The impact of cross-platform development approaches for mobile applications from the user’s perspective. In Proceedings of the International Workshop on App Market Analytics, , , and (Eds.). ACM, 43–49.
DOI: Google ScholarDigital Library - [162] . 2019. A multi-label active learning approach for mobile app user review classification. In Proceedings of the 12th International Conference on Knowledge Science, Engineering and Management(
Lecture Notes in Computer Science , Vol. 11775), , , and (Eds.). Springer, 805–816.DOI: Google ScholarDigital Library - [163] . 2013. The state of the art in automated requirements elicitation. Inf. Softw. Technol. 55, 10 (2013), 1695–1709.Google ScholarDigital Library
- [164] . 2019. Speech-acts based analysis for requirements discovery from online discussions. Inf. Syst. 86 (2019), 94–112.
DOI: Google ScholarDigital Library - [165] . 2017. Natural language insights from code reviews that missed a vulnerability. In Engineering Secure Software and Systems, , , and (Eds.). Springer International Publishing, Cham, 70–86.Google ScholarCross Ref
- [166] . 2018. A cognitive agent for mining bugs reports, feature suggestions and sentiment in a mobile application store. In Proceedings of the 4th International Conference on Big Data Innovations and Applications, Innovate-Data. IEEE, 17–24.
DOI: Google ScholarCross Ref - [167] . 2018. An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems. Empir. Softw. Eng. 23, 1 (2018), 521–564.
DOI: Google ScholarDigital Library - [168] . 2014. Do developers feel emotions? An exploratory analysis of emotions in software artifacts. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR’14). ACM, 262–271.
DOI: Google ScholarDigital Library - [169] . 2013. Fast and accurate sentiment classification using an enhanced naive Bayes model. In Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning(
Lecture Notes in Computer Science , Vol. 8206). Springer, 194–201.DOI: Google ScholarDigital Library - [170] . 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP’03), , , and (Eds.). ACM, 70–77.
DOI: Google ScholarDigital Library - [171] . 2018. App store mining is not enough for app improvement. Empir. Softw. Eng. 23, 5 (2018), 2764–2794.
DOI: Google ScholarDigital Library - [172] . 2017. Crowdsourced exploration of mobile app features: A case study of the Fort McMurray wildfire. In Proceedings of the IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS). 57–66.
DOI: Google ScholarDigital Library - [173] . 2019. EmoD: An end-to-end approach for investigating emotion dynamics in software development. In Proceedings of the International Conference on Software Maintenance and Evolution. IEEE, 252–256.
DOI: Google ScholarCross Ref - [174] . 2019. Healthcare Android apps: A tale of the customers’ perspective. In Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics, and (Eds.). ACM, 33–39.
DOI: Google ScholarDigital Library - [175] . 2011. A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC2011 Workshop on “Making Sense of Microposts”: Big Things Come in Small Packages. 93–98.Google Scholar
- [176] . 2019. A survey of utilizing user-reviews posted on Google Play Store. In Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering. 54–63.Google ScholarDigital Library
- [177] . 2019. Too many user-reviews, what should app developers look at first? IEEE Trans. Softw. Eng. 47, 2 (2019), 1–1.
DOI: Google ScholarDigital Library - [178] . 2020. Can we use SE-specific sentiment analysis tools in a cross-platform setting? In Proceedings of the IEEE/ACM 17th International Conference on Mining Software Repositories. 158–168.
DOI: Google ScholarDigital Library - [179] . 2015. The challenges of sentiment detection in the social programmer ecosystem. In Proceedings of the 7th International Workshop on Social Software Engineering, and (Eds.). ACM, 33–40.
DOI: Google ScholarDigital Library - [180] . 2018. A gold standard for emotion annotation in stack overflow. In Proceedings of the 15th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 14–17.
DOI: Google ScholarDigital Library - [181] . 2021. Assessment of off-the-shelf SE-specific sentiment analysis tools: An extended replication study. Empir. Softw. Eng. 26 (2021), 77:1–29.Google ScholarDigital Library
- [182] . 2018. A benchmark study on sentiment analysis for software engineering research. In Proceedings of the 15th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 364–375.
DOI: Google ScholarDigital Library - [183] . 2019. Sentiment and emotion in software engineering. IEEE Softw. 36, 5 (2019), 6–23.Google ScholarDigital Library
- [184] . 2021. Development and application of sentiment analysis tools in software engineering: A systematic literature review. In Proceedings of the Conference on Evaluation and Assessment in Software Engineering. Association for Computing Machinery, New York, NY, 80–89.
DOI: Google ScholarDigital Library - [185] . 2015. Are bullies more productive? Empirical study of affectiveness vs. issue fixing time. In Proceedings of the 12th IEEE/ACM Working Conference on Mining Software Repositories, , , and (Eds.). IEEE Computer Society, 303–313.
DOI: Google ScholarCross Ref - [186] . 2016. Arsonists or firefighters? Affectiveness in agile software development. In Proceedings of the 17th International Conference on Agile Processes, in Software Engineering, and Extreme Programming(
Lecture Notes in Business Information Processing , Vol. 251), and (Eds.). Springer, 144–155.DOI: Google ScholarCross Ref - [187] . 2018. Mining communication patterns in software development: A GitHub analysis. In Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, , , and (Eds.). ACM, 70–79.
DOI: Google ScholarDigital Library - [188] . 2019. Empirical analysis of affect of merged issues on GitHub. In Proceedings of the 4th International Workshop on Emotion Awareness in Software Engineering. IEEE/ACM, 46–48.
DOI: Google ScholarDigital Library - [189] . 2016. The emotional side of software developers in JIRA. In Proceedings of the 13th International Conference on Mining Software Repositories (MSR’16). ACM, 480–483.
DOI: Google ScholarDigital Library - [190] . 2016. The emotional side of software developers in JIRA. In Proceedings of the 13th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 480–483.
DOI: Google ScholarDigital Library - [191] . 2017. Automated classification of software issue reports using machine learning techniques: An empirical study. Innov. Syst. Softw. Eng. 13, 4 (2017), 279–297.
DOI: Google ScholarDigital Library - [192] . 2007. Opinion mining and sentiment analysis. Found. Trends Inf Retr. 2, 1–2 (2007), 1–135.
DOI: Google ScholarDigital Library - [193] . 2015. How can I improve my app? Classifying user reviews for software maintenance and evolution. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME’15). IEEE Computer Society, 281–290.
DOI: Google ScholarDigital Library - [194] . 2015. How can I improve my app? Classifying user reviews for software maintenance and evolution. In Proceedings of the International Conference on Software Maintenance and Evolution, , , and (Eds.). IEEE Computer Society, 281–290.
DOI: Google ScholarDigital Library - [195] . 2016. ARdoc: App reviews development oriented classifier. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, , , and (Eds.). ACM, 1023–1027.
DOI: Google ScholarDigital Library - [196] . 2013. Sentiment analysis of user comments for one-class collaborative filtering over TED talks. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 773–776.Google ScholarDigital Library
- [197] . 2017. Sentiment identification for collaborative, geographically dispersed, cross-functional software development teams. In Proceedings of the 3rd IEEE International Conference on Collaboration and Internet Computing. IEEE Computer Society, 20–26.
DOI: Google ScholarCross Ref - [198] . 2019. Expressions of sentiments during code reviews: Male vs. Female. In Proceedings of the 26th IEEE International Conference on Software Analysis, Evolution and Reengineering, , , and (Eds.). IEEE, 26–37.
DOI: Google ScholarCross Ref - [199] . 2015. Is this code written in English? A study of the natural language of comments and identifiers in practice. In Proceedings of the IEEE International Conference on Software Maintenance and Evolution, , , and (Eds.). IEEE Computer Society, 401–410.
DOI: Google ScholarDigital Library - [200] . 2016. An approach of extracting feature requests from app reviews. In Proceedings of the 12th International Conference on Collaborate Computing: Networking, Applications and Worksharing(
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , Vol. 201), and (Eds.). Springer, 312–323.DOI: Google ScholarCross Ref - [201] . 2019. Classification of mobile application user reviews for generating tickets on issue tracking system. In Proceedings of the 12th International Conference on Information Communication Technology and System (ICTS). 229–234.
DOI: Google ScholarCross Ref - [202] . 2014. Security and emotion: Sentiment analysis of security discussions on GitHub. In Proceedings of the 11th Working Conference on Mining Software Repositories, , , and (Eds.). ACM, 348–351.
DOI: Google ScholarDigital Library - [203] . 2018. Usability related qualities through sentiment analysis. In Proceedings of the 1st International Workshop on Affective Computing for Requirements Engineering, , , and (Eds.). IEEE, 20–26.
DOI: Google ScholarCross Ref - [204] . 2016. SatiIndicator: Leveraging user reviews to evaluate user satisfaction of sourceforge projects. In Proceedings of the 40th IEEE Annual Computer Software and Applications Conference. IEEE Computer Society, 93–102.
DOI: Google ScholarCross Ref - [205] . 2016. Evaluating quality-in-use of FLOSS through analyzing user reviews. In Proceedings of the 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, (Ed.). IEEE Computer Society, 547–552.
DOI: Google ScholarCross Ref - [206] . 2015. Recommending insightful comments for source code using crowdsourced knowledge. In Proceedings of the 15th IEEE International Working Conference on Source Code Analysis and Manipulation, , , and (Eds.). IEEE Computer Society, 81–90.
DOI: Google ScholarCross Ref - [207] . 2015. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowl.-based Syst. 89 (2015), 14–46.
DOI: Google ScholarDigital Library - [208] . 2019. Leveraging small software engineering data sets with pre-trained neural networks. In Proceedings of the 41st International Conference on Software Engineering: New Ideas and Emerging Results., and (Eds.). IEEE/ACM, 29–32.
DOI: Google ScholarDigital Library - [209] . 2016. Developer behavior and sentiment from data mining open source repositories. In Proceedings of the 49th Hawaii International Conference on System Sciences, and (Eds.). IEEE Computer Society, 3729–3738.
DOI: Google ScholarDigital Library - [210] . 2019. Taking the emotional pulse of software engineering—A systematic literature review of empirical studies. Inf. Softw. Technol. 115 (2019), 23–43.Google ScholarDigital Library
- [211] . 2018. Analyzing the impact of feedback in GitHub On the software developer’s mood. In Proceedings of the 30th International Conference on Software Engineering and Knowledge Engineering, (Ed.). KSI Research Inc. and Knowledge Systems Institute Graduate School, 445–444.
DOI: Google ScholarCross Ref - [212] . 2020. A network-centric approach for estimating trust between open source software developers. PLoS One 14, 12 (
12 2020), 1–30.DOI: Google ScholarCross Ref - [213] . 2019. Listening to the crowd for the release planning of mobile apps. IEEE Trans. Softw. Eng. 45, 1 (2019), 68–86.
DOI: Google ScholarDigital Library - [214] . 2019. Listening to the crowd for the release planning of mobile apps. IEEE Trans. Softw. Eng. 45, 1 (2019), 68–86.
DOI: Google ScholarDigital Library - [215] . 2015. Automatically mining negative code examples from software developer Q & A forums. In Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering Workshops. IEEE Computer Society, 115–122.
DOI: Google ScholarDigital Library - [216] . 2016. Feature-based evaluation of competing apps. In Proceedings of the International Workshop on App Market Analytics, , , and (Eds.). ACM, 15–21.
DOI: Google ScholarDigital Library - [217] . 2019. Using app reviews for competitive analysis: Tool support. In Proceedings of the 3rd ACM SIGSOFT International Workshop on App Market Analytics, and (Eds.). ACM, 40–46.
DOI: Google ScholarDigital Library - [218] . 2019. Evaluating the performance of machine learning sentiment analysis algorithms in software engineering. In Proceedings of the International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress. IEEE, 1023–1030.
DOI: Google ScholarCross Ref - [219] . 2017. Understanding feature requests by leveraging fuzzy method and linguistic analysis. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 440–450.
DOI: Google ScholarCross Ref - [220] . 2017. How do code refactoring activities impact software developers’ sentiments?—An empirical investigation into GitHub commits. In Proceedings of the 24th Asia-Pacific Software Engineering Conference, , , , and (Eds.). IEEE Computer Society, 648–653.
DOI: Google ScholarCross Ref - [221] . 2016. Analyzing developer sentiment in commit logs. In Proceedings of the 13th International Conference on Mining Software Repositories (MSR’16). ACM, 520–523.
DOI: Google ScholarDigital Library - [222] . 2016. Analyzing developer sentiment in commit logs. In Proceedings of the 13th International Conference on Mining Software Repositories, , , and (Eds.). ACM, 520–523.
DOI: Google ScholarDigital Library - [223] . 2019. Analysis of newcomers activity in communicative posts on GitHub. In Digital Transformation and Global Society, , , , , , and (Eds.). Springer International Publishing, Cham, 452–460.Google ScholarCross Ref
- [224] . 2012. Pattern for Python. J. Mach. Learn. Res. 13 (2012), 2063–2067. Retrieved from http://dl.acm.org/citation.cfm?id=2343710.Google Scholar
- [225] . 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’13). ACL, 1631–1642. Retrieved from https://www.aclweb.org/anthology/D13-1170/.Google Scholar
- [226] . 2016. What would users change in my app? Summarizing app reviews for recommending software changes. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, , , and (Eds.). ACM, 499–510.
DOI: Google ScholarDigital Library - [227] . 2015. Development emails content analyzer: Intention mining in developer discussions. In Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 12–23.
DOI: Google ScholarDigital Library - [228] . 2017. Sentiment analysis of Travis CI builds. In Proceedings of the 14th International Conference on Mining Software Repositories, , , and (Eds.). IEEE Computer Society, 459–462.
DOI: Google ScholarDigital Library - [229] . 2019. Classifying multilingual user feedback using traditional machine learning and deep learning. In Proceedings of the 27th IEEE International Requirements Engineering Conference Workshops. IEEE, 220–226.
DOI: Google ScholarCross Ref - [230] . 2018. Extracting useful software development information from mobile application reviews: A survey of intelligent mining techniques and tools. Exp. Syst. Applic. 113 (2018), 186–199.Google ScholarDigital Library
- [231] . 2017. TensiStrength: Stress and relaxation magnitude detection for social media texts. Inf. Process. Manag. 53, 1 (2017), 106–121.
DOI: Google ScholarCross Ref - [232] . 2010. Sentiment strength detection in short informal text. J. Assoc. Inf. Sci. Technol. 61, 12 (2010), 2544–2558.
DOI: Google ScholarCross Ref - [233] . 2016. The impact of human discussions on just-in-time quality assurance: An empirical study on openstack and eclipse. In Proceedings of the IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering. IEEE Computer Society, 189–200.
DOI: Google ScholarCross Ref - [234] . 2014. Monitoring sentiment in open source mailing lists: Exploratory study on the Apache ecosystem. In Proceedings of 24th Annual International Conference on Computer Science and Software Engineering, , , and (Eds.). IBM/ACM, 34–44. Retrieved from http://dl.acm.org/citation.cfm?id=2735528.Google Scholar
- [235] . 2019. Topics of concern: Identifying user issues in reviews of IoT apps and devices. In Proceedings of the 1st International Workshop on Software Engineering Research & Practices for the Internet of Things. IEEE/ACM, 33–40.
DOI: Google ScholarDigital Library - [236] . 2019. Automatic mining of opinions expressed about APIs in stack overflow. IEEE Trans. Softw. Eng. 122 (2019), 1–1.
DOI: Google ScholarCross Ref - [237] . 2020. Mining API usage scenarios from stack overflow. Inf. Softw. Technol. 122 (2020), 106277.
DOI: Google ScholarCross Ref - [238] . 2016. Release planning of mobile apps based on user reviews. In Proceedings of the 38th International Conference on Software Engineering (ICSE’16). ACM, 14–24.
DOI: Google ScholarDigital Library - [239] . 2015. Tool support for analyzing mobile app reviews. In Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, , , and (Eds.). IEEE Computer Society, 789–794.
DOI: Google ScholarDigital Library - [240] . 2019. A systematic mapping study on crowdsourced requirements engineering using user feedback. J. Softw.: Evol. Process 31, 10 (2019), e2199.Google ScholarDigital Library
- [241] . 2019. Extracting API tips from developer question and answer websites. In Proceedings of the 16th International Conference on Mining Software Repositories, , , and (Eds.). IEEE/ACM, 321–332.
DOI: Google ScholarDigital Library - [242] . 2019. Emotions extracted from text vs. true emotions-an empirical evaluation in SE context. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering. IEEE, 230–242.
DOI: Google ScholarDigital Library - [243] . 2017. Extracting user-reported mobile application defects from online reviews. In Proceedings of the IEEE International Conference on Data Mining Workshops, , , , , , , , and (Eds.). IEEE Computer Society, 422–429.
DOI: Google ScholarCross Ref - [244] . 2018. The evolution of emotional displays in open source software development teams: An individual growth curve analysis. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering. ACM, 1–6.
DOI: Google ScholarDigital Library - [245] . 2018. The evolution of emotional displays in open source software development teams: An individual growth curve analysis. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, , , and (Eds.). ACM, 1–6.
DOI: Google ScholarDigital Library - [246] . 2018. MEME: Toward a method for emotions extraction from GitHub. In Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, , , and (Eds.). ACM, 20–24.
DOI: Google ScholarDigital Library - [247] . 2019. Can a machine learn through customer sentiment?: A cost-aware approach to predict support ticket escalations. IEEE Softw. 36, 5 (2019), 38–45.
DOI: Google ScholarDigital Library - [248] . 2019. What can the sentiment of a software requirements specification document tell us? In Proceedings of the 27th IEEE International Requirements Engineering Conference Workshops. IEEE, 106–107.
DOI: Google ScholarCross Ref - [249] . 2018. How angry are your customers? Sentiment analysis of support tickets that escalate. In Proceedings of the 1st International Workshop on Affective Computing for Requirements Engineering, , , and (Eds.). IEEE, 1–8.
DOI: Google ScholarCross Ref - [250] . 2017. Analyzing, classifying, and interpreting emotions in software users’ tweets. In Proceedings of the 2nd IEEE/ACM International Workshop on Emotion Awareness in Software Engineering. IEEE Computer Society, 2–7.
DOI: Google ScholarCross Ref - [251] . 2017. Mining Twitter feeds for software user requirements. In Proceedings of the 25th IEEE International Requirements Engineering Conference, , , , and (Eds.). IEEE Computer Society, 1–10.
DOI: Google ScholarCross Ref - [252] . 2012. Experimentation in Software Engineering. Springer Science & Business Media.Google ScholarCross Ref
- [253] . 2021. BERT for sentiment classification in software engineering. In Proceedings of the International Conference on Service Science (ICSS). 115–121.
DOI: Google ScholarCross Ref - [254] . 2017. Sentiments analysis in GitHub repositories: An empirical study. In Proceedings of the 24th Asia-Pacific Software Engineering Conference Workshops-. IEEE, 84–89.
DOI: Google ScholarCross Ref - [255] . 2017. A method to transform automatically extracted product features into inputs for Kano-like models. In 18th International Conference on Product-Focused Software Process Improvement(
Lecture Notes in Computer Science , Vol. 10611), , , , , , and (Eds.). Springer, 237–254.DOI: Google ScholarCross Ref - [256] . 2018. The OIRE method—Overview and initial validation. In Proceedings of the 25th Asia-Pacific Software Engineering Conference. IEEE, 1–10.
DOI: Google ScholarCross Ref - [257] . 2020. Sentiment analysis for software engineering: How far can pre-trained transformer models go? In Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME). 70–80.
DOI: Google ScholarCross Ref - [258] . 2013. Extracting problematic API features from forum discussions. In Proceedings of the IEEE 21st International Conference on Program Comprehension. IEEE Computer Society, 142–151.
DOI: Google ScholarCross Ref - [259] . 2019. Sentiment analysis based requirement evolution prediction. Fut. Internet 11, 2 (2019), 52.
DOI: Google ScholarCross Ref - [260] . 2011. Comparing Twitter and traditional media using topic models. In Proceedings of the 33rd European Conference on IR Research on Advances in Information Retrieval(
Lecture Notes in Computer Science , Vol. 6611). Springer, 338–349.DOI: Google ScholarDigital Library - [261] . 2019. A domain knowledge incorporated text mining approach for capturing user needs on BIM applications. Eng., Construct. Archit. Manag. 27, 2 (2019).Google ScholarCross Ref
- [262] . 2013. Assessing software quality through web comment search and analysis. In 13th International Conference on Software Reuse: Safe and Secure Software Reuse(
Lecture Notes in Computer Science , Vol. 75), and (Eds.). Springer, 208–223.DOI: Google ScholarCross Ref
Index Terms
- Opinion Mining for Software Development: A Systematic Literature Review
Recommendations
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementAspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...
Feature and Opinion Mining for Customer Review Summarization
PReMI '09: Proceedings of the 3rd International Conference on Pattern Recognition and Machine IntelligenceIn this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is ...
A Systematic Literature Review on Global Software Development Life Cycle
Global software development (GSD) has now become a prominent software development paradigm. Software companies are increasingly adopting GSD approaches in order to produce high quality software. GSD's popularity has attracted the researchers to ...
Comments