Sentimental Analysis and Keyword Extraction from Thai Users of Facebook in COVID-19 Period
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Abstract
The new outbreak of Covid-19 leads to the confusion and has many effects on people including change in lifestyle and income opportunity. With the campaign of social distancing and stay-home, it becomes difficult to obtain people opinion using traditional methods such as questionnaire and interview. This work thus applies natural language processing tasks including keyword detection and sentimental analysis to analyze a trend of topics and their feelings towards the new outbreak of Covid-19 from Facebook posts. Detected keywords represent significant topics people were frequently mentioned while sentiment helps to reveal people feeling towards the topics. This study collected Facebook data related to Covid-19 posted between 1st November 2020 and 5th January, 2021 from those setting home location in Three Southern Border Provinces of Thailand for 3,127 posts. The keyword detection results indicate that there are four main groups of terms frequently discussed online as terms related disease prevention, terms about worry, terms about healthcare and treatment, and terms related to government policy. From results of sentimental analysis, the collected posts were separated to 80.07% negative posts, 14.42% positive posts and 15.45% neutral posts. Majority of the negative posts (46%) was about feeling frustrated of illegal activities causing the new outbreak whilst 56% of the positive posts were feeling impressive and praising towards fast attempt on prevention of Covid-19 spreading.
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