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The challenges of sentiment detection in the social programmer ecosystem

Published: 01 September 2015 Publication History

Abstract

A recent research trend has emerged to study the role of affect in in the social programmer ecosystem, by applying sentiment analysis to the content available in sites such as GitHub and Stack Overflow. In this paper, we aim at assessing the suitability of a state-of-the-art sentiment analysis tool, already applied in social computing, for detecting affective expressions in Stack Overflow. We also aim at verifying the construct validity of choosing sentiment polarity and strength as an appropriate way to operationalize affective states in empirical studies on Stack Overflow. Finally, we underline the need to overcome the limitations induced by domain-dependent use of lexicon that may produce unreliable results.

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Cited By

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  • (2024)Semantic Web Approaches in Stack OverflowInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.35861720:1(1-61)Online publication date: 9-Nov-2024
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    cover image ACM Conferences
    SSE 2015: Proceedings of the 7th International Workshop on Social Software Engineering
    September 2015
    52 pages
    ISBN:9781450338189
    DOI:10.1145/2804381
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    Published: 01 September 2015

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    Author Tags

    1. Online Q&A
    2. Sentiment Analysis
    3. Social Programmer
    4. Social Software Engineering
    5. Stack Overflow
    6. Technical Forum

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    • (2024)Supporting Developers' Emotional Awareness: from Self-reported Emotions to BiometricsProceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering10.1145/3661167.3661209(500-504)Online publication date: 18-Jun-2024
    • (2024)Mining crowd sourcing repositories for open innovation in software engineeringAutomated Software Engineering10.1007/s10515-023-00410-z31:1Online publication date: 8-Jan-2024
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