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Twittener: Improving News Experience with Sentiment Analysis and Trend Recommendation

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Social Computing and Social Media (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14703))

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Abstract

The Internet provides a profusion of online sources for both trending topics and news. With the vast content made available, it might risk readers in information overloading before finding all relevant contents and thus perceive this medium as challenging and overwhelming. Most of the available news sites provide content from official news sites and exclude posts on social media. This paper presents a web application, Twittener, an improved news aggregator that enhances users’ reading experience and time-efficiency when reading news online, with the implementation of text-to-speech technology, sentiment analysis and hybrid recommender system. This paper also presents a user study that was conducted to determine the factors that increase the acceptance rate of such a system by the public based on the Technology Acceptance Model (TAM).

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Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, 639798, Singapore

    Jie Jun Tien, Wei Jie Heng & Owen Noel Newton Fernando

Authors
  1. Jie Jun Tien
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  2. Wei Jie Heng
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  3. Owen Noel Newton Fernando
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Corresponding author

Correspondence to Wei Jie Heng .

Editor information

Editors and Affiliations

  1. University of Bucharest, Bucharest, Romania

    Adela Coman

  2. University of Tsukuba, Tsukuba, Japan

    Simona Vasilache

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Tien, J.J., Heng, W.J., Fernando, O.N.N. (2024). Twittener: Improving News Experience with Sentiment Analysis and Trend Recommendation. In: Coman, A., Vasilache, S. (eds) Social Computing and Social Media. HCII 2024. Lecture Notes in Computer Science, vol 14703. Springer, Cham. https://doi.org/10.1007/978-3-031-61281-7_30

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  • DOI: https://doi.org/10.1007/978-3-031-61281-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61280-0

  • Online ISBN: 978-3-031-61281-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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