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Sentiment Analysis on Big News Media Data

In: Social Big Data Analytics

Author

Listed:
  • Bilal Abu-Salih

    (The University of Jordan)

  • Pornpit Wongthongtham

    (The University of Western Australia)

  • Dengya Zhu

    (Curtin University)

  • Kit Yan Chan

    (Curtin University)

  • Amit Rudra

    (Curtin University)

Abstract

Sentiment Analysis (aka Opinion Mining) intends to discover public opinions and sentiments towards other entities (Liu B, Sentiment analysis and opinion mining, Synthesis lectures on human language technologies, vol. 5. Morgan & Claypool Publishers, p 167, 2012). In recent years, while the number of public opinions, reviews and comments are exploding on the Web, the cost of accessing these data via the Internet is declining. Consequently, sentiment analysis has not only become an active research area, but also being widely employed by organizations and enterprises to gain financial benefits. This chapter demonstrates how to apply big data technologies to keep track of sentiments and opinions expressed in public news media on given topics, such as, real-estate market in Australia. First, we introduce basic concepts of sentiment analysis, neural networks and deep learning; then, follow that up by describing the big data framework used – a Hadoop cluster employed in our project. This cluster facilitates data crawling from the Web and then, processes the accumulated data. Further, we describe the approaches and the models utilized in our research, including the experimental design employed. Finally, we present our research outcome by means of a list of tables and figures to demonstrate how big data techniques can successfully reveal news media’s sentiments towards Australia’s real-estate market from different angles based on our big news media data collected from the Web.

Suggested Citation

  • Bilal Abu-Salih & Pornpit Wongthongtham & Dengya Zhu & Kit Yan Chan & Amit Rudra, 2021. "Sentiment Analysis on Big News Media Data," Springer Books, in: Social Big Data Analytics, chapter 0, pages 177-218, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-6652-7_7
    DOI: 10.1007/978-981-33-6652-7_7
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