Sentiment Polarity Detection for Software Development

Calefato, Fabio; Lanubile, Filippo; Maiorano, Federico; Novielli, Nicole

doi:10.1007/s10664-017-9546-9

Cited by 179 publications

(104 citation statements)

References 58 publications

(101 reference statements)

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“…Among the identified difficulties, lacking domain-specific knowledge is demonstrated to be the most dominant, accounting for about 81% of the classification errors. Since then, how to effectively leverage SE-related texts to introduce technical jargon becomes the main direction of the sentiment analysis in SE [8,14,41]. Against such a background, many SE-customized sentiment analysis tools and methods are proposed, including SentiStrength-SE [35], Senti-CR [8], Senti4SD [14], etc.…”

Section: Sentiment Analysis In Sementioning

confidence: 99%

“…Since then, how to effectively leverage SE-related texts to introduce technical jargon becomes the main direction of the sentiment analysis in SE [8,14,41]. Against such a background, many SE-customized sentiment analysis tools and methods are proposed, including SentiStrength-SE [35], Senti-CR [8], Senti4SD [14], etc. We take them along with the most popular SentiStrength as baseline methods in this study and introduce them detailedly in Section 4.1.…”

Section: Sentiment Analysis In Sementioning

confidence: 99%

“…Furthermore, Imtiaz et al [34] directly used emojis as the indicators of developers' sentiments on GitHub. Calefato et al [14] and Ding et al [22] took emoticons into account in their proposed sentiment analysis techniques built on SE-related texts. All of them demonstrated the feasibility of leveraging these emotional cues to benefit sentiment analysis in SE.…”

Section: Emojis In Sentiment Analysismentioning

confidence: 99%

“…Furthermore, Islam and Zibran [35] applied SentiStrength to an SErelated dataset and found that misunderstanding of domain-specific meanings of words (namely technical jargon in the rest of this paper) accounts for the most misclassifications. Such a finding inspires a series of research efforts in recent years to generate SE-specific datasets and develop customized sentiment analysis methods based on them [8,14,41]. Various machine-learning or deep-learning arXiv:1907.02202v1 [cs.SE] 4 Jul 2019 techniques have been applied, but the performance still falls short of producing adequate results in practice for some SE tasks [41].…”

Section: Introductionmentioning

confidence: 99%

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SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

Chen

Cao

Lü

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.

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Section: Sentiment Analysis In Sementioning

confidence: 99%

Section: Sentiment Analysis In Sementioning

confidence: 99%

Section: Emojis In Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

Chen

Cao

Lü

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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“…SVM is one of the most investigated approaches for statistical document classification and it is considered stateof-the-art [25,21]. Moreover, it showed good results in software engineering-specific text classification problems (e.g., [26,27]). SVM finds the hyper-plane maximizing the margin between two classes in the feature space, and it can learn and generalize high-dimensional features typical for text classification tasks [21,25].…”

Section: Traditional Machine Learningmentioning

confidence: 99%

On using machine learning to identify knowledge in API reference documentation

Fucci

Mollaalizadehbahnemiri

Maalej

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Using API reference documentation like JavaDoc is an integral part of software development. Previous research introduced a grounded taxonomy that organizes API documentation knowledge in 12 types, including knowledge about the Functionality, Structure, and Quality of an API. We study how well modern text classification approaches can automatically identify documentation containing specific knowledge types. We compared conventional machine learning (k-NN and SVM) and deep learning approaches trained on manually-annotated Java and .NET API documentation (n = 5,574). When classifying the knowledge types individually (i.e., multiple binary classifiers) the best AUPRC was up to 87%. The deep learning and SVM classifiers seem complementary. For four knowledge types (Concept, Control, Pattern, and Non-Information), SVM clearly outperforms deep learning which, on the other hand, is more accurate for identifying the remaining types. When considering multiple knowledge types at once (i.e., multi-label classification) deep learning outperforms naïve baselines and traditional machine learning achieving a MacroAUC up to 79%. We also compared classifiers using embeddings pre-trained on generic text corpora and StackOverflow but did not observe significant improvements. Finally, to assess the generalizability of the classifiers, we re-tested them on a different, unseen Python documentation dataset. Classifiers for Functionality, Concept, Purpose, Pattern, and Directive seem to generalize from Java and .NET to Python documentation. The accuracy related to the remaining types seems API-specific. We discuss our results and how they inform the development of tools for supporting developers sharing and accessing API knowledge. Published article: https://doi.org/10.1145/3338906.3338943Software developers reuse software libraries and frameworks through Application Programming Interfaces (APIs). They often rely on reference documentation to identify which API elements are relevant for the task at hand, how the API can be instantiated, configured, and combined [1]. Compared to other knowledge sources, such as tutorials and Q&A portals, reference documentation like JavaDoc and PyDoc are considered the official API technical documentation. They provide detailed and fundamental information about API elements, components, operations, and structures [2, 3]. Figure 1: A reference documentation page in the JDK API annotated with the knowledge types it contains. DirectiveAs API documentation can be thousands of pages long [4,5], accessing relevant knowledge can be tedious and timeconsuming [1]. Moreover, the information necessary to accomplish a task can be scattered across the documentation pages of multiple elements, such as classes, methods, and properties. Thus, developers try to use other sources to fulfill their information needs. For example, although the Java Development Kit (JDK) API documentation contains more than 7,000 pages, as of early 2019, there are more than 3 million StackOverflow posts tagged as java.Over the last decade, sof...

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Improving the detection of community smells through socio‐technical and sentiment analysis

Almarimi

Ouni

Chouchen

et al. 2022

J Software Evolu Process

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Open source software development is regarded as a collaborative activity in which developers interact to build a software product. Such a human collaboration is described as an organized effort of the "social" activity of organizations, individuals, and stakeholders, which can affect the development community and the open source project health. Negative effects of the development community manifest typically in the form of community smells, which represent symptoms of organizational and social issues within the open source software development community that often lead to additional project costs and reduced software quality. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational, social, and emotional aspects to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizationalsocial and emotional symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 10 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems, which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 143 open source projects. The statistical tests of our results show that our approach can detect community smells with an average Fmeasure of 93%, achieving a better performance compared to different state-of-theart techniques. Furthermore, we investigate the most influential community-related metrics to identify each community smell type.human factors in software engineering, software organization and properties | INTRODUCTIONOpen source software (OSS) development is a collaborative activity in which developers interact between each other to develop and maintain a software system. 1,2 Nowadays, OSS is becoming an essential part in the software economy. The number of OSS projects and their size has been growing at exponential rate. 3 The success of OSS development leads to building sustainable software systems. 2,4,5 Unlike proprietary software, OSS projects can be developed and maintained by the whole community, providing more transparency on the source code, development processes, and technologies as well as developers communications. As there exist a substantial number of OSS projects, there is a continuous

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Sentiment Polarity Detection for Software Development

Cited by 179 publications

References 58 publications

SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

On using machine learning to identify knowledge in API reference documentation

Improving the detection of community smells through socio‐technical and sentiment analysis

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