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Incorporating Pre-trained Transformer Models into TextCNN for Sentiment Analysis on Software Engineering Texts

Published: 15 September 2022 Publication History

Abstract

Software information sites (e.g., Jira, Stack Overflow) are now wide-ly used in software development. These online platforms for collaborative development preserve a large amount of Software Engineering (SE) texts. These texts enable researchers to detect developers’ attitudes toward their daily development by analyzing the sentiments expressed in the texts. Unfortunately, recent works reported that neither off-the-shelf tools nor SE-specified tools for sentiment analysis on SE texts can provide satisfying and reliable results. In this paper, we propose to incorporate pre-trained transformer models into the sentence-classification oriented deep learning framework named TextCNN to better capture the unique expression of sentiments in SE texts. Specifically, we introduce an optimized BERT model named RoBERTa as the word embedding layer of TextCNN, along with additional residual connections between RoBERTa and TextCNN for better cooperation in our training framework. An empirical evaluation based on four datasets from different software information sites shows that our training framework can achieve overall better accuracy and generalizability than the four baselines.

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            Internetware '22: Proceedings of the 13th Asia-Pacific Symposium on Internetware
            June 2022
            291 pages
            ISBN:9781450397803
            DOI:10.1145/3545258
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            Published: 15 September 2022

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            1. Nature Language Processing
            2. Pre-trained Models
            3. Sentiment Analysis
            4. Software Mining

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            • The National Natrual Science Foundation of China
            • the National Key Research and Development Program of China
            • The Research Council of Norway
            • the Collaborative Innovation Center of Novel Software Technology and Industrialization
            • Intergovernmental Bilateral Innovation Project of Jiangsu Province

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