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研究生:高煜智
研究生(外文):KAO, YU-CHIH
論文名稱:利用潛在狄利克雷分配法解析美國汽車網路評論以探索近年美國汽車工業趨勢之研究
論文名稱(外文):Analyzing the online reviews to explore recent trends of the U.S. car industry by Latent Dirichlet Allocation method.
指導教授:鄭明顯鄭明顯引用關係
指導教授(外文):CHENG, MING-SHIEN
口試委員:戴弘政鄭明顯陳琨太
口試委員(外文):TAI, HUNG-CHENGCHENG, MING-SHIENCHEN, KUEN-TAI
口試日期:2023-07-20
學位類別:碩士
校院名稱:明志科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:78
中文關鍵詞:汽車工業趨勢網路評論文字探勘潛在狄利克雷分配
外文關鍵詞:Automotive Industry TrendsOnline ReviewText MiningLatent Dirichlet Allocation
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隨著網際網路的普及與社交媒體的興起,網路評論是提供人們在做任何決定的重要資料,這也造就了網路評論的數量龐大、多樣性和動態性。在網路評論的研究中,大多學者以電影業或旅遊業為研究對象,唯汽車或鐘錶等高價值產業更需要研究分析供人們能夠參考,其中汽車工業是具有極高的產業關聯性和擴及作用,對國家經濟有著巨大的推動力,另外美國又是有著汽車產業巨人之稱的重要國家。因此本研究挑選美國汽車產業為研究對象,探討近年來汽車發展趨勢。因為會以評論內容為分析來源,所以挑選文本分析的主題模型,並以主題模型中最具代表性的潛在狄利克雷分配(Latent Dirichlet Allocation, LDA)為方法建模。
研究方法的部分會從資料蒐集開始,首先抓取2020年、2021年、2022年共7492筆美國汽車網路評論,並將這些資料進行預處理後進入設定好的LDA模型,再參考LDA相關的評估指標進行調整,最後再將產出結果進行市場與相關文獻的資料分析。
本研究結果如下:在市場分析中,全部年份的駕駛感受、舒適、內裝、油耗等詞彙與美國汽車媒體相關的性價比排名皆有相關,還有從2020年的Truck與SUV車型、2021和2022年SUV與Hybrid車型、2022年銷售數據的Tesla電動車,可得出Hybrid與純電車的逐年成長趨勢。在文獻分析中,全部年份的詞彙中有駕駛感受、舒適、內裝等,也與過去學者研究相關汽車趨勢的文獻相符合,可得出即使選用的網站與國家不盡相同,但在近年的汽車網路評論中,討論的趨勢也是有著一定相似度與關聯性的。因此也證實LDA在美國汽車網路評論中是可信且可靠的。
With the rise of the Internet and the increasing popularity of social media, online reviews have become crucial sources of information for decision-making. This has resulted in a vast and diverse collection of dynamic online reviews. While previous research on online reviews has primarily focused on industries such as movies or travel, it is essential to study and analyze high-value industries like the automotive or watch industry to provide valuable insights for consumers. Moreover, due to its high interrelatedness and far-reaching effects on the national economy, the automotive industry holds significant importance as a driving force. Additionally, as a global leader in the automotive sector, the U.S. automotive industry has been selected as the subject of this study to explore recent trends in automotive development. The chosen methodology for the analysis is a topic model for textual analysis, and Latent Dirichlet Allocation (LDA) is considered the most representative topic model within this framework.
A total of 7,492 online reviews of the U.S. automotive industry from the years 2020, 2021, and 2022 were collected and preprocessed to prepare them for input into the pre-set LDA model. The LDA model was adjusted, taking into account relevant evaluation metrics associated with LDA. Finally, the output results were analyzed in conjunction with market insights and relevant literature in the field.
The study's results are as follows: In the market analysis, driving experience, comfort, interior, gas mileage, and other terms for all years are correlated with price/performance rankings in the U.S. automotive media. Based on the analysis of the 2020 Truck and SUV, the 2021 and 2022 SUV and Hybrid models, as well as the sales data of Tesla electric vehicles in 2022, a growing trend of Hybrid and pure electric vehicles could be observed. The common words across all years included "comfortable," "interior," and " driving experience," which align with previous studies on trends in the automotive industry. These findings suggest that, despite differences in selected websites and countries, certain similarities and correlations exist in recent online automotive reviews. Consequently, it also confirms the credibility and reliability of LDA in analyzing U.S. automotive online reviews.
目錄
指導教授推薦書 i
口試委員會審定書 ii
誌謝 iii
摘要 iv
Abstract vi
目錄 viii
圖目錄 xi
表目錄 xiii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 5
2.1 網路評論 5
2.1.1 網路評論定義 5
2.1.2 網路評論的影響力 6
2.1.3 專家與非專家網路評論 7
2.2 汽車工業與汽車評論 9
2.2.1 汽車工業的重要性 9
2.2.2 汽車評論與趨勢相關研究 11
2.3 文本分析 13
2.3.1 文字探勘 13
2.3.2 文字探勘的流程與應用 14
2.4 潛在狄利克雷分配 16
2.4.1 LDA定義 16
2.4.2 LDA的相關研究 22
2.4.3 LDA在不同領域的應用 27
第三章 研究方法 29
3.1 研究流程 29
3.2 資料蒐集 31
3.3 資料處理 34
3.3.1 資料預處理 34
3.3.2 定義LDA模型 36
3.3.3 訓練LDA模型 36
3.4 資料分析 41
3.4.1 LDA模型評估 41
3.4.2 調整模型或數據 48
3.4.3 結果分析 49
第四章 研究結果 50
4.1 LDA產出結果 50
4.1.1 全部年份之產出結果 51
4.1.2 2020年產出結果 52
4.1.3 2021年產出結果 53
4.1.4 2022年產出結果 54
4.2 結果分析 55
4.2.1 市場分析 55
4.2.2 文獻分析 59
第五章 結論與建議 60
5.1 結論 60
5.2 研究限制 62
5.3 未來研究建議 62
參考文獻 64
中文文獻 64
英文文獻 69


圖目錄
圖 1 研究架構 4
圖 2 LDA主題與字詞以顏色區分 17
圖 3 LDA模型概念示意圖 18
圖 4 LDA生產過程概念示意圖 19
圖 5 Generative model for latent Dirichlet allocation 25
圖 6 研究流程圖 30
圖 7 Cars.com網站 31
圖 8 Cars.com網站之車款分類查詢系統 32
圖 9 Cars.com網站之汽車評論區 32
圖 10 利用網路爬蟲於Cars.com網站抓取之原始資料 33
圖 11 前處理之刪除數字、標點符號、去除空格後的資料 34
圖 12 前處理之使用EnStemmer後的資料 35
圖 13 預處理完成之文字檔 35
圖 14 LDA之主題數量困惑度差異折線圖 38
圖 15 LDA之反覆運算次數困惑度差異折線圖 39
圖 16 LDA之隨機種子困惑度差異折線圖 40
圖 17 LDA困惑度之年份差異散布圖 43
圖 18 LDA一致性分數(c_v)之年份差異散布圖 43
圖 19 LDA一致性分數(c_uci)之年份差異散布圖 43
圖 20 LDA主題一致性之年份差異折線圖 47
圖 21 LDA產出結果 49
圖 22 LDA視覺化模型 49
圖 23 全部年份之LDA視覺化模型 51
圖 24 2020年之LDA視覺化模型 52
圖 25 2021年之LDA視覺化模型 53
圖 26 2022年之LDA視覺化模型 54


表目錄
表 1 LDA模型示意圖符號表 18
表 2 LDA文檔產生過程公式符號表 19
表 3 LDA之主題數量差異表 38
表 4 LDA之反覆運算次數差異表 39
表 5 LDA之隨機種子差異表 40
表 6 LDA之年份差異表 42
表 7 LDA主題間重疊度之年份差異表 44
表 8 LDA主題一致性之年份差異表 47
表 9 全部年份之LDA詞彙 55
表 10 2020年LDA詞彙 56
表 11 2020年美國地區汽車銷售數據(依車型) 56
表 12 2021年LDA詞彙 57
表 13 2021年美國地區汽車銷售數據(依車型) 57
表 14 2022年LDA詞彙 58
表 15 2022年美國地區汽車銷售數據(依車型) 58
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