@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00231340,
 author = {Shao, Hsuan-Lei and Hsuan-Lei, Shao},
 book = {じんもんこん2023論文集},
 month = {Dec},
 note = {This  study  aims  to  explore  the  challenges  of  the  "Taiwan  issue"  for  Xi  Jinping's  third  term.  Besides  the international structural factors of geopolitical dynamics, the 'resist will' within Taiwanese society is identified as a decisive element. Consequently, this research proposes a novel methodology and process to measure and understand the variations in  Taiwan's  'resist  will'  across  different scenarios.  Traditional  survey  methods  have  limitations  in  capturing  audience perceptions and reactions to various contexts. Therefore, this study employs text mining to analyze online forum discourse, designing scenario-specific queries to more accurately gauge the resist will. Compared to traditional approaches, this method facilitates a more comprehensive understanding of audience emotions and attitudes, achieving a standardized process. The findings indicate significant variations in the resist will across different contexts. For instance, scenarios requiring personal sacrifice tend to elicit more negative emotions from the audience, whereas those positively impacting national capabilities are likely to provoke positive emotions. By analyzing the changes in the “resist will” across various scenarios, as well as the stance and sentiment of social media posts, the study aims to delve into the factors influencing Taiwanese people's display of resistance. Specifically, the research calculates the positive and negative sentiment values of articles to determine their stance; in conjunction with 'article attention' and 'comment support' metrics, it analyzes social media  responses  to  further  understand  reader  emotions  and  positions.  This  approach  not  only  breaks  through  the constraints  of  traditional  survey  methods  but  also  achieves  real-time  responsiveness  and  systematic  data  processing. Moreover, the flexibility of the research method makes it applicable to various contexts and needs, offering insights for related studies and policy formulation., This  study  aims  to  explore  the  challenges  of  the  "Taiwan  issue"  for  Xi  Jinping's  third  term.  Besides  the international structural factors of geopolitical dynamics, the 'resist will' within Taiwanese society is identified as a decisive element. Consequently, this research proposes a novel methodology and process to measure and understand the variations in  Taiwan's  'resist  will'  across  different scenarios.  Traditional  survey  methods  have  limitations  in  capturing  audience perceptions and reactions to various contexts. Therefore, this study employs text mining to analyze online forum discourse, designing scenario-specific queries to more accurately gauge the resist will. Compared to traditional approaches, this method facilitates a more comprehensive understanding of audience emotions and attitudes, achieving a standardized process. The findings indicate significant variations in the resist will across different contexts. For instance, scenarios requiring personal sacrifice tend to elicit more negative emotions from the audience, whereas those positively impacting national capabilities are likely to provoke positive emotions. By analyzing the changes in the “resist will” across various scenarios, as well as the stance and sentiment of social media posts, the study aims to delve into the factors influencing Taiwanese people's display of resistance. Specifically, the research calculates the positive and negative sentiment values of articles to determine their stance; in conjunction with 'article attention' and 'comment support' metrics, it analyzes social media  responses  to  further  understand  reader  emotions  and  positions.  This  approach  not  only  breaks  through  the constraints  of  traditional  survey  methods  but  also  achieves  real-time  responsiveness  and  systematic  data  processing. Moreover, the flexibility of the research method makes it applicable to various contexts and needs, offering insights for related studies and policy formulation.},
 pages = {31--36},
 publisher = {情報処理学会},
 title = {What if War in Taiwan–Applying Machine Learning to Analyze the “Resist Will”},
 volume = {2023},
 year = {2023}
}