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論文中文名稱:基於主題與時間序列模型之社群主題趨勢預測 [以論文名稱查詢館藏系統]
論文英文名稱:Trend Forecasting for Social Topics Based on Topic and Time Series Model [以論文名稱查詢館藏系統]
英文姓名:Huai-Wen Hsu
英文關鍵詞:Trend ForecastingTopic ModelTime Series ModelSocial Media
論文英文摘要:The rapid growth of the social media leads people participate in the popular topics which are being discussed and fermented in our lives by the social networks. Large amounts of word-of-mouth and news events have flooded the social media. Recognizing the trends of the main topics that people care about from the huge and miscellaneous social messages, grasping the business opportunities and adopt appropriate strategies become an important lesson for business, governmental and non-governmental organizations.
Previous researches on social topic detection have focused on sentiment analysis for content. This study integrates the topic detection model and time series model to forecast trends of the social topics based on time series data of user reviews. Based on the experimental results on real dataset, this study can recognize the latent social topics, keywords and forecast the trend of each topic effectively on the PTT. In this research, the average MAPE of this model is 3.9% and indicates that our approach can reach pretty good accuracy.
論文目次:摘要 I
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
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 章節概要與研究流程 5
第二章 文獻探討 6
2.1 社群媒體(SOCIAL MEDIA) 6
2.1.1 批踢踢實業坊(PTT) 6
2.1.2 社群媒體行銷(Social Media Marketing) 7
2.1.3 社群口碑與社群探勘(Social Word-of-Mouth and Social Mining) 8
2.2.1 以關鍵字為基礎之主題偵測 (Keyword-based Topic Detection) 9
2.2.2 以分群方法為基礎之主題偵測(Clustering-based Topic Detection) 10
2.2.3 以主題模型為基礎之主題偵測(Topic Model-based Topic Detection) 11
2.3.1 以統計模型為基礎之熱門主題與趨勢預測 12
2.3.2 以機器學習方法為基礎之熱門主題與趨勢預測 13
第三章 研究方法 16
3.1 研究架構 16
3.2 資料蒐集 17
3.3 資料預處理 17
3.3.1 斷詞處理 18
3.3.2 停用詞過濾 18
3.4 主題發現與偵測 20
3.5 主題熱門度與趨勢預測 23
3.5.1 熱門狀態轉移與輸出參數 23
3.5.2 主題熱門度預測模型 27
第四章 實驗結果分析與討論 32
4.1 實驗環境 32
4.2 實驗設計 32
4.3 實驗資料集與前置處理 33
4.4 實驗評估方法 36
4.5 實驗結果與討論 37
4.5.1 主題模型在不同主題數設定下之差異 37
4.5.2 不同時間區間長度之留言序列對文章積分預測之效果比較 39
4.5.3 不同訓練資料比例對文章積分預測之效果比較 41
4.5.4 實際案例與各主題平均預測結果比較 42
第五章 結論 44
5.1 研究結論與貢獻 44
5.2 研究限制與未來展望 45
參考文獻 46
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