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A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networks

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Abstract

The substantial volume of user feedback contained in application reviews significantly contributes to the development of human-centred software requirement engineering. The abundance of unstructured text data necessitates an automated analytical framework for decision-making. Language models can automatically extract fine-grained aspect-based sentiment information from application reviews. Existing approaches are constructed based on the general domain corpus, and are challenging to elucidate the internal technique of the recognition process, along with the factors contributing to the analysis results. To fully utilize software engineering domain-specific knowledge and accurately identify aspect-sentiment pairs from application reviews, we design a dependency-enhanced heterogeneous graph neural networks architecture based on the dual-level attention mechanism. The heterogeneous information network with knowledge resources from the software engineering field is embedded into graph convolutional networks to consider the attribute characteristics of different node types. The relationship between aspect terms and sentiment terms in application reviews is determined by adjusting the dual-level attention mechanism. Semantic dependency enhancement is introduced to comprehensively model contextual relationships and analyze sentence structure, thereby distinguishing important contextual information. To our knowledge, this marks initial efforts to leverage software engineering domain knowledge resources to deep neural networks to address fine-grained sentiment analysis issues. The experimental results on multiple public benchmark datasets indicate the effectiveness of the proposed automated framework in aspect-based sentiment analysis tasks for application reviews.

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Data availability

No datasets were generated or analysed during the current study. https://www.kaggle.com/datasets.

Code availability

https://github.com/zouhaochen/Fine-Grained_Sentiment_Analysis.

Notes

  1. https://play.google.com/store/.

  2. https://www.apple.com/app-store/.

  3. https://www.kaggle.com/.

  4. https://github.com/amiangshu/SentiSE/.

  5. https://huggingface.co/BAAI/llm-embedder/.

  6. https://github.com/cjhutto/vaderSentiment/.

  7. https://github.com/stanfordnlp/.

  8. https://github.com/cbaziotis/ekphrasis/.

  9. https://huggingface.co/.

  10. http://stanza.run/.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This article has been supported by the National Natural Science Foundation of China (61941113), Science and Technology on Information System Engineering Laboratory (No. 05202104). Jiangsu Province Key R&D Program (Modern Agriculture) Key Project (BE2023352), Key Medical Research Projects of Jiangsu Provincial Health Commission (ZD2022068).

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This article has been supported by the National Natural Science Foundation of China (61941113), Science and Technology on Information System Engineering Laboratory (No: 05202104).

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  1. Haochen Zou and Yongli Wang have contributed equally to this work.

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  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, 210094, Jiangsu, China

    Haochen Zou & Yongli Wang

  2. Department of Computer Science and Software Engineering, Concordia University, 2155 Guy Street, Montreal, QC, H3H 2L9, Canada

    Haochen Zou

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Conceptualization, HZ and YW; methodology, YW; software, HZ; validation, YW; formal analysis, HZ; investigation, HZ; resources, YW; data curation, HZ; writing-original draft preparation, HZ; writing-review and editing, HZ; visualization, HZ; project administration, YW. All authors have read and agreed to the published version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yongli Wang.

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Zou, H., Wang, Y. A novel automated framework for fine-grained sentiment analysis of application reviews using deep neural networks. Autom Softw Eng 31, 43 (2024). https://doi.org/10.1007/s10515-024-00444-x

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