Facebook、Twitter 和 Instagram 等社群媒體平台如今越來越受歡迎，因為它們讓使用者可以在網路上創作、分享資訊和評論與日常生活相關的任何內容。此類訊息對許多不同領域特別有用，例如線上廣告等。因此，對貼文及回應的社交網路分析已成為一個重要的研究領域。以前的研究側重於分析貼文及回應的正負性，而不是它們之間的關係。因此，本研究提出了一個多目標優化框架來研究貼文及回應之間的關係。在此框架中，我們將情感詞典和 TF-IDF 組合用於查找可以代表貼文及回應的關鍵字，再將 Fuzzy C-Means (FCM) 方法用於將相似的貼文及回應合併為數個群集。再套用多目標優化演算法 NSGA-III 用於優化本研究的三個目標：（1）文章隸屬程度平均變異值，（2）留言隸屬程度平均變異值，以及（3）預測留言結果平均均方根誤差。本研究試圖透過提出的框架尋找貼文與其對應的回應，因為透過找出特定關鍵字引起的特定回應以及將收到的回應類型，我們可以預測貼文將得到的回應種類。研究結果表明，所提出的框架可以幫助找出貼文及回應之間的關係。後續與單目標優化算法 GA 的進一步比較證實了NSGA-III 需要獲得可為調查提供洞察力的結果。

Online social media platforms like Facebook, Twitter, and Instagram are getting
more and more popular today because they allow users to create, share, and comment
on anything connected to their daily lives. Such information is particularly helpful for many different fields, such as online advertising. Therefore, social network analysis on posts and comments has become an important research area. Previous studies focused on analyzing the positive or negative polarity of posts and comments, however not the relationships between them. In this research, a multi-objective optimization framework was proposed to investigate the relationship between posts and comments. A
combination of sentiment lexicons and TF-IDF were used to find keywords that could
represent posts and comments. Fuzzy C-Means (FCM) clustering method was used to
cluster similar posts and comments together. We try to find out what comments will be
the response to the posts. Because by finding out what keywords were eliciting
responses from other users and the type of reactions they will receive, we can predict
what kind of responses each post will have. This is useful for marketing or sales team
to analyze what keywords would attract the attention of consumers or customers.
NSGA-III was utilized to optimize the three objectives of this research: (1) the average variance of post membership, (2) the average variance of comment membership, and (3) the root-mean-square error of prediction. The results of the research shows that the framework proposed can help to find the relationship between posts and comments.
Further comparison with single-objective optimization algorithm, GA, shows the
advantages of using NSGA-III to solve this research problem.

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