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研究生:陳奎伯
研究生(外文):Kuei-Po Chen
論文名稱:基於社群媒體意見預測市場偏好服務套裝
論文名稱(外文):Forecasting Market Preference of Service Packages Based on Opinions in Social Media
指導教授:陳信希陳信希引用關係
口試委員:陳昇瑋曾元顯蔡銘峰
口試日期:2019-07-16
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:51
中文關鍵詞:市場偏好推薦系統迴歸模型情感分析服務套裝
DOI:10.6342/NTU201902660
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本研究提出一個創新的方法,將所有產品皆表達成為元素組合的服務套裝形式,並根據當時的社群網站評論意見,進行市場喜好分析,預測次季熱門的服務套裝,推薦給企業行銷部門做為調整產品內容的參考方向。透過比較上市產品的實際銷售紀錄以及模型預測的銷售結果,兩年間共八季的實驗數據顯示,本研究的分析方法產生的推薦排序相關性超越傳統銷售預測的效能,因而能夠做為服務套裝產品連結輿情分析的解決方案。
This study proposes an innovative method, which presents all the products in packages of multiple elements and conducts market preference analysis based on the opinions in social media at the time collected by Web Crawler, to forecast popular packages for advising marketing departments on the adjustment of product content. An experiment is made to compare the actual sales records by a large enterprise during two years with the sales data predicted by the trained model that can forecast the next quarter according to the relevant online opinions about the previous quarter. With the comparison results of eight quarters during the two years, this analysis method proves to surpass the efficiency of traditional sales forecasting approaches, and therefore can be the solutions of public opinion analysis for service packages.
誌謝 i
中文摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables vii

Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Explanation of Terms 2
1.2.1 Market Preference 2
1.2.2 Service Packages 2
1.3 Problem Statement 3
1.4 Contributions 4
1.5 Thesis Organization 4

Chapter 2 Related Work 6
2.1 Recommendation 6
2.2 Chinese Sentiment Analysis 8

Chapter 3 Datasets 10
3.1 Sales Data of Service Packages 10
3.1.1 Data Exploration 10
3.1.2 Data Preprocessing 14
3.2 The Public Opinion Corpus 14
3.2.1 Data Exploration 14
3.2.2 Data Preprocessing 16
3.2.2.1 Objectivity Detection 17
3.2.2.2 Abnormal User Detection 20
3.2.2.3 Vocabulary Construction 22
3.2.2.4 Coreference Resolution 24

Chapter 4 Feature Extraction for the Proposed Model 27
4.1 Document Level 27
4.2 Sentence Level 28
4.3 Entity Level 29
4.4 Structure of the Proposed Model 31

Chapter 5 Experiments 33
5.1 Experimental Setup 33
5.2 Evaluation Metrics 33
5.3 Baseline Models for Comparison 35
5.3.1 Trained by Sales Data 35
5.3.2 Trained by Sentiment Scores from SnowNLP Toolkit 36
5.3.3 Trained by Sales Data and Sentiment Scores 38
5.4 Results and Analysis 39
5.4.1 NDCG Evaluation Results 39
5.4.2 Performance Comparison of Baseline Models 40
5.4.3 Performance Comparison of the Proposed Model 42
5.4.4 Evaluation Comparison of NDCG@50 and NDCG@100 44

Chapter 6 Conclusion and Future Work 46
6.1 Conclusion 46
6.2 Future Work 48
References 49
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