Sentiment Analysis and Summarization of Facebook Posts on News Media

Yin-Chun Fung, Lap-Kei Lee, Kwok Tai Chui, Gary Hoi-Kit Cheung, Chak-Him Tang, Sze-Man Wong

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationData Mining Approaches for Big Data and Sentiment Analysis in Social Media
Number of pages13
Publication statusPublished - 2021


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