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The Study of Predicting Social Topic Trends

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Advances in E-Business Engineering for Ubiquitous Computing (ICEBE 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 41))

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Abstract

The rapid growth of the social media leads people participate in the popular topics that have been discussed in our daily lives by the social networks. Large amounts of word-of-mouth and news event have flood the social media. Recognizing the trends of the main topics that people care about from the huge and various social messages, grasping the business opportunities and adopting appropriate strategies have become an important lesson for business, governmental and non-governmental organizations. Previous research on social topic detection has focused on sentiment analysis for content. This study integrates the hidden markov model and latent dirichlet allocation topic model to forecast trends of the social topics based on time series data of user reviews. Experimental results on real dataset showed that the approach proposed by this study are able to recognize the latent social topics, keywords and forecast the trends of social topics effectively on the social media.

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Notes

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Author information

Authors and Affiliations

  1. National Taipei University of Technology, Taipei, 10608, Taiwan

    Sung-Shun Weng & Huai-Wen Hsu

Authors
  1. Sung-Shun Weng
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  2. Huai-Wen Hsu
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Corresponding author

Correspondence to Sung-Shun Weng .

Editor information

Editors and Affiliations

  1. ECB, Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Kuo-Ming Chao

  2. School of Software, Shanghai Jiaotong University, Shanghai, China

    Lihong Jiang

  3. School of Business, University of New South Wales, Canberra, ACT, Australia

    Omar Khadeer Hussain

  4. National Taiwan Ocean University, Keelung City, Taiwan

    Shang-Pin Ma

  5. Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Xiang Fei

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Weng, SS., Hsu, HW. (2020). The Study of Predicting Social Topic Trends. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_18

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