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Sentiment Analysis of Mobile Apps Using BERT

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Advances and Trends in Artificial Intelligence. Theory and Applications (IEA/AIE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13926))

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Abstract

In this paper, we focus on identifying issues in mobile app updates that adversely impact the opinion in user reviews by analyzing the sentiment of the reviews. We use sentiment analysis using BERT to evaluate the performance of mobile apps and the sentiment distribution of reviews for identifying the cause of sentiment shifts. Using our method, developers can correctly locate the period of specific sentiment and review the sentences and keywords used in reviews to identify the problems and complaints in recent updates. An increase in negative sentiments after any major update can help identify the exact issue causing the problem. Our experimental analysis shows the effectiveness of the proposed method in recognizing issues and identifying any potential problematic updates.

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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    https://github.com/JoMingyu/google-play-scraper.

  3. 3.

    https://pypi.org/project/langdetect/.

  4. 4.

    https://www.businesstoday.in/technology/news/story/whatsapp-changes-everything-with-its-new-status-feature-71508-2017-02-21.

  5. 5.

    https://www.greenbot.com/microsoft-completely-revamps-skype-new-ui-snapchat-like-stories/.

  6. 6.

    https://www.apkmirror.com/apk/google-inc/hangouts/hangouts-17-0-148298972-release/hangouts-17-0-148298972-17-android-apk-download/.

  7. 7.

    https://www.apkmirror.com/apk/facebook-2/messenger/messenger-100-0-0-29-61-release/.

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Acknowledgement

This work is based on the Master Thesis “Sentiment Analysis of Mobile Apps Using BERT” [16], appearing at the digital library of the Tampere University. We thank the Tampere University for supporting this research.

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Authors and Affiliations

  1. Tampere University, Tampere, Finland

    Wajhee Ullah, Zheying Zhang & Kostas Stefanidis

Authors
  1. Wajhee Ullah
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  2. Zheying Zhang
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  3. Kostas Stefanidis
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Corresponding author

Correspondence to Wajhee Ullah .

Editor information

Editors and Affiliations

  1. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Hamido Fujita

  2. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

  3. Fudan University, Shanghai, China

    Yanghua Xiao

  4. Texas State University, San Marcos, TX, USA

    Ali Moonis

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Ullah, W., Zhang, Z., Stefanidis, K. (2023). Sentiment Analysis of Mobile Apps Using BERT. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13926. Springer, Cham. https://doi.org/10.1007/978-3-031-36822-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-36822-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36821-9

  • Online ISBN: 978-3-031-36822-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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