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Sentiment Analysis and Summarization of Facebook Posts on News Media

Sentiment Analysis and Summarization of Facebook Posts on News Media
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Author(s): Yin-Chun Fung (Hong Kong Metropolitan University, Hong Kong), Lap-Kei Lee (Hong Kong Metropolitan University, Hong Kong), Kwok Tai Chui (Hong Kong Metropolitan University, Hong Kong), Gary Hoi-Kit Cheung (Hong Kong Metropolitan University, Hong Kong), Chak-Him Tang (Hong Kong Metropolitan University, Hong Kong) and Sze-Man Wong (Hong Kong Metropolitan University, Hong Kong)
Copyright: 2022
Pages: 13
Source title: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India), Dragan Peraković (University of Zagreb, Croatia), Ahmed A. Abd El-Latif (Menoufia University, Egypt) and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-8413-2.ch006

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Abstract

Social media has become part of daily life in the modern world. News media companies (NMC) use social network sites including Facebook pages to let net users keep updated. Public expression is important to NMC for making valuable journals, but it is not cost-effective to collect millions of feedback by human effort, which can instead be automated by sentiment analysis. This chapter presents a mobile application called Facemarize that summarizes the contents of news media Facebook pages using sentiment analysis. The sentiment of user comments can be quickly analyzed and summarized with emotion detection. The sentiment analysis achieves an accuracy of over 80%. In a survey with 30 participants including journalists, journalism students, and journalism graduates, the application gets at least 4.9 marks (in a 7-point Likert scale) on the usefulness, ease of use, ease of learning, and satisfaction with a mean reliability score of 3.9 (out of 5), showing the effectiveness of the application.

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