球隊認同感代表著球迷對於特定球隊實際上的感受，大部分的研究會透過問卷及量表進行評估，可能會遇到有些球迷隱藏自己的身份，誇大或虛答球隊的認同感，已獲得個人的利益，使用問卷量表將無法有效評估真實的球隊認同感；隨著社群媒體的崛起，球迷們現在擁有更多的機會在線上社交平台上與他們喜愛的球隊建立更密切的互動關係。這些互動活動產生的數據和信息提供了研究者 一個全新的管道，可以深入研究球迷的行為和情感 ，並進一步理解他們對於特定球隊的認同感。本論文嘗試將Branscombe and Wann(1992)所提出 的球隊認同感量表中的七個核心問題轉化為社群媒體上的數據指標，以反映球迷在線上的言論和互動。通過K-means機器學習的分群演算法，成功地識別出五種不同類型的球迷類型，這些類型具有不同的球迷特性。球團和體育組織可以通過利用社群媒體數據、文字探勘技術和情緒分析工具來評估球迷的對於球隊的認同感。分析結果可以幫助他們更好地了解球迷的需求、興趣和情感傾向，並能夠在不同的時間和事件情境下分析不同類型球迷特性和行為模式。最後依照球迷特性透過創建不同類型球迷機器人，讓球團更有效地進行預測受眾反映來制定粉絲互動策略，加強社群媒體上與球迷間的連結。

In the past, team identification was typically assessed and evaluated using scales. However, with the rise of social media, fans now have more opportunities to establish closer interactions with their favorite teams on online platforms. The data and information generated from these interactive activities present a unique opportunity to delve into the behaviors and emotions of fans, further enhancing our understanding of their sense of identification with specific sports teams.
This study attempts to transform the seven core questions from Branscombe and Wann's (1992) team identification scale into social media data indicators, reflecting fans' online statements and interactions. Using the K-means machine learning clustering algorithm, five different types of fan categories with distinct characteristics were successfully identified.Sports teams and organizations can assess fans' team identification by utilizing social media data, text mining techniques, and sentiment analysis tools. Analyzing these results can help them better understand the needs, interests, and emotional tendencies of fans and analyze different types of fan characteristics and behavior patterns in various times and event contexts.
Finally, by creating different types of fan bots based on fan characteristics, teams can more effectively predict audience reactions to formulate fan interaction strategies, strengthening the connection with fans on social media.

第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3研究範圍與限制 5
第二章 文獻探討 6
2.1認同感與球隊認同感 6
2.2社群媒體 8
2.3機器學習與文字探勘 10
2.3.1 機器學習 10
2.3.2 K-means 演算法 11
2.3.3 文字探勘應用 12
2.4生成式語言模型 12
第三章 研究方法 15
3.1研究流程 15
3.2研究對象 15
3.3資料集 16
3.4資料前處理 17
3.4.1 資料清洗 17
3.4.2 特徵轉換 19
3.4.3 情緒模型標註 22
3.5 球隊認同感量表轉換 23
3.6 資料分析 25
3.7 建置球迷聊天機器人 25
第四章 研究分析與結果 28
4.1 K-Means 分群演算法結果 28
4.1.1 K值選擇 29
4.1.2 視覺化分群結果 29
4.1.3 不同類型球迷 31
4.2 不同類型球迷在社群媒體中的球迷特性 35
4.2.1 低認同感球迷的特性 36
4.2.2 中認同感球迷的特性 42
4.2.3 高認同感球迷的特性 47
4.2.4 有影響力的球迷的特性 49
4.2.5 高度參與的球迷的特性 51
4.3 不同類型的球迷聊天機器人 53
4.3.1 低認同感球迷的聊天機器人 53
4.3.2 中認同感球迷的聊天機器人 55
4.3.3 高認同感球迷的聊天機器人 58
4.3.4 有影響力球迷的聊天機器人 59
4.3.5 高度參與球迷的聊天機器人 61
第五章 研究結論與建議 64
附錄一 建置聊天機器人設定 67
參考文獻 70

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