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dc.contributor.authorYan-Rong Laien_US
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dc.description.abstract網際網路的蓬勃發展帶動社群媒體的興起,目前,社群媒體已經無處不在,在當今的商業環境中扮演著越來越重要的角色,其所生成的用戶內容為企業的產品策略提供重要的資訊,也因此,社群媒體分析 (social media analytics) 成為一熱門研究領域。然而在過去社群媒體分析的相關文獻中,多僅以用戶評論做為分析內容,且鮮少整合話題情感性與話題冷熱門作探討。因此,本研究挖掘了擴增實境遊戲-精靈寶可夢GO (Pokémon GO) 之Facebook粉絲專頁的247則貼文與45萬筆留言回覆,以潛在狄利克里分配法 (Latent Dirichlet Allocation, LDA) 為基礎,分別分析用戶評論以及官方貼文的關鍵主題,透過比對雙方主題及關鍵詞的差異,發覺細節資訊,並找出官方所忽視的部分。另外,更透過整合所獲主題之冷熱門主題分析與情緒分析的結果,了解用戶對於產品之冷、熱門議題的態度,以發展未來產品的改善方向。研究結果發現,在用戶評論方面發現了地點配置問題、手機裝置與兼容問題、遊戲內裝扮與儲存空間等問題,為官方貼文未注意到的主題,顯示出了用戶評論的珍貴價值;此外,我們也就時間趨勢了解活動疏漏補償、要求對戰模式改善、與遊戲登入問題等是用戶在意且顯著熱門的議題;最後,本研究針對各主題計算其情緒分數,在整合情緒分析與冷熱門主題分析結果後,發現活動疏漏補償、要求新對戰模式、寶可夢雷達功能不良等主題,為亟需改善的產品議題,提供了未來設計其他遊戲產品時所應考慮的要素。zh_TW
dc.description.abstractThe flourish of Internet leads to the rise of social media. Nowadays, social media plays an important role in business, because companies can obtain the critical information from user-generated content in social media. Therefore, social media analytics becomes a hot research area. However, while the user-generated content in social media has been extensively investigated, the official posts is relatively unexplored. Also, the integration of sentiment analysis and hot/cold topic analysis has not been applied in social media yet. Thus, this research scrawls 247 posts and 450,000 comments from the Facebook fan page of augmented reality game - Pokémon GO. Latent Dirichlet Allocation (LDA), an unsupervised machine learning method, is used to analyze key topics of official posts and user comments, respectively. Further, sentiment analysis and hot/cold topic analysis are applied to understand the trend of topics and the user's emotional attitude toward the topics. The research results reveal that the topics, such as the location allocation problem, mobile device and compatibility issues, dressing function and storage space in games, are not found in official posts, but in user comments. In addition, the hot topic analysis is used to find that users put more focus on the topics such as the compensation of activities omission, request for the battle system improvement, and the login problem. Finally, the sentiment score is calculated for each topic and then integrate the results of hot/cold topic analysis and sentiment analysis. The results show that the topics, such as the compensation of activities omission, request for new battle mode, and the dysfunction of radar, are the most important issues which need to be improved. The research findings can also be used for improving other augmented reality games.en_US
dc.description.tableofcontents摘要 i Abstract ii 表目錄 iv 圖目錄 v 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機 1 第三節 研究目的 2 第二章 文獻回顧 4 第一節 社群媒體分析 (social media analytics) 4 第二節 潛在狄利克里分配法 (Latent Dirichlet Allocation, LDA) 7 第三節 情緒分析 (sentiment analysis) 8 第三章 研究方法 9 第一節 研究架構 9 第二節 資料蒐集 10 第三節 資料預處理(data pre-processing) 11 第四節 LDA主題分析 12 第五節 貼文-心情分析 13 第六節 冷熱門主題分析 13 第七節 情緒分析 14 第四章 研究結果 16 第一節 社群貼文之主題分布 16 第二節 留言評論之主題分布 18 第三節 主題命名驗證 21 第四節 貼文-留言雙邊主題比較 22 第五節 貼文-心情分析 24 第六節 冷熱門主題分析 26 第七節 情緒分析 28 第八節 總結 30 第五章 結論與建議 32 第一節 主要發現 32 第二節 管理意涵 32 第三節 研究貢獻 33 第四節 研究限制與未來研究方向 34 參考文獻 34 附錄A 貼文主題命名驗證統計 39 附錄B 留言主題命名驗證統計 40zh_TW
dc.subjectsocial media analyticsen_US
dc.subjectaugmented reality gamesen_US
dc.subjectLatent Dirichlet Allocation (LDA)en_US
dc.subjecthot/cold topic analysisen_US
dc.subjectsentiment analysisen_US
dc.titleUsing Social Media Analytics to Explore User Comments' Topics: A Case Study of Pokémon GOen_US
dc.typethesis and dissertationen_US
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