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  • 标题:A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier
  • 本地全文:下载
  • 作者:Nawapon Kewsuwun ; Siriwan Kajornkasirat
  • 期刊名称:International Journal of Electrical and Computer Engineering
  • 电子版ISSN:2088-8708
  • 出版年度:2022
  • 卷号:12
  • 期号:3
  • 页码:2829-2838
  • DOI:10.11591/ijece.v12i3.pp2829-2838
  • 语种:English
  • 出版社:Institute of Advanced Engineering and Science (IAES)
  • 摘要:Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.
  • 关键词:Agritech startup;Facebook;Naive Bayes;Python;sentiment analysis
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