臺灣博碩士論文加值系統

全球智慧型手機的出貨量持續提升，且熱門市集的App下載次數紛紛突破500億次。而在iOS和Android手機App市集中，App的評價和評論對App在市集的排序有很大的影響；對於App開發者而言，透過評論確實可掌握使用者的需求，並在產生抱怨前能快速反應避免危機。然而，每日多達上百篇的評論，透過人力逐篇查看，不止耗費時間，更無法整合性的瞭解使用者的需求與問題。
文字情感分析通常會使用監督式或非監督式的方法分析文字評論，其中監督式方法被證實透過簡單的文件量化方法就可達到很高的正確率。但監督式方法有無法預期未知趨勢的限制，且需要進行耗費人力的文章類別標注工作。
本研究透過情感傾向和熱門關注議題兩個面向來分析App評論，提出一個混合非監督式與監督式的中文情感分析方法。我們先透過非監督式方法標注評論類別，並作視覺化整理呈現，最後再用監督式方法建立分類模型，並驗證其效果。
在實驗結果中，利用中文詞彙網路所建立的情感詞集，確實可用來判斷評論的正反情緒，唯判斷負面評論效果不佳需作改善。在議題擷取方面，嘗試使用兩種不同分群方法，其中使用NPMI衡量字詞間關係強度，再配合社群網路分析的Concor方法結果有不錯的成效。最後在使用監督式學習的分類結果中，情感傾向的分類正確率達到87%，關注議題的分類正確率達到96%，皆有不錯表現。
本研究利用中文詞彙網路與社會網路分析，來發展一個非監督式的中文類別判斷方法，並建立一個中文情感分析的範例。另外透過建立全面性的視覺化報告來瞭解使用者的正反回饋意見，並可透過分類模型來掌握新評論的內容，以提供App開發者在市場上之競爭智慧。

While the number of smartphone shipment is continuesly growing, the number of App downloads from the popular app markets has been already over 50 billion. By Apple App Store and Google Play, ratings and reviews play a more important role in influencing app difusion. While app developers can realize users’ needs by app reviews, more than thousands of reviews produced by user everday become difficult to be read and collated.
Sentiment Analysis researchs encompass supervised and unsupervised methods for analyzing review text. The supervised learning is proven as a useful method and can reach high accuracy, but there are limits where future trend can not be recognized and the labels of individual classes must be made manually.
We concentrate on two issues, viz Sentiment Orientation and Popular Topic, to propose a Chinese Sentiment Analysis method which combines supervised and unsupervised learning. At First, we use unsupervised learning to label every review articles and produce visualized reports. Secondly, we employee supervised learning to build classification model and verify the result.
In the experiment, the Chinese WordNet is used to build sentiment lexicon to determin review’s sentiment orientation, but the result shows it is weak to find out negative review opinions. In the Topic Extraction phase, we apply two clustering methods to extract Popular Topic classes and its result is excellent by using of NPMI Model with Social Network Analysis Method i.e. Concor. In the supervised learning phase, the accuracy of Sentiment Orientation class is 87% and the accuracy of Popular Topic class is 96%.
In this research, we conduct an exemplification of the unsupervised method by means of Chinese WorkNet and Social Network Analysis to determin the review classes. Also, we build a comprehensive visualized report to realize users’ feedbacks and utilize classification to explore new comments. Last but not least, with Chinese Sentiment Analysis of this research, and the competitive intelligence in App market can be provided to the App develops.

摘要 ii
Abstract iii
圖目錄 vi
表目錄 viii
第一章、 概論 1
1、 研究背景 1
2、 研究動機 3
3、 研究目的 4
4、 研究方法 6
第二章、 文獻探討 8
1、 行動裝置與手機市集的發展 8
1.1 行動裝置的發展 8
1.2 手機作業系統與App市集的發展 9
2、 情感分析(Sentiment Analysis) 11
2.1 情感分析的分類 11
2.2 情感分析的方法 13
2.3 情感分析的應用 19
3、 特徵詞選取(Feature Selection) 21
3.1 文件頻率(Document Frequency Threshold) 21
3.2 訊息增益(Information Gain) 22
3.3 交互資訊量(Mutual Information) 23
3.4 卡方統計量(Chi-Square Statistic) 24
3.5 不同特徵詞選取方法的比較 25
4、 文字分類(Text Classification) 27
4.1 簡單貝氏分類器(Naïve Bayes Classifier) 27
4.2 kNN(k- Nearest Neighbor) 29
4.3 支援向量機(Support Vector Machine, SVM) 30
4.4 不同分類方法的比較 32
5、 對應分析(Correspondence Analysis) 34
第三章、 研究方法 35
1、 資料蒐集(Data Collection) 36
2、 評論文章前處理(Document Preprocessing) 37
3、 評論情感傾向計算(Sentiment Orientation) 41
3.1 建立情感詞集(Building Sentiment Term Set) 41
3.2 計算評論的情感傾向 43
4、 評論議題擷取(Topic Extraction) 44
5、 視覺化分析(Visualization) 48
6、 建立向量空間模型(Vector Space Model) 49
7、 特徵詞萃取(Feature Selection) 51
8、 分類模型建立與分類成效衡量(Classification) 52
8.1 監督式學習的分類演算法 52
8.2 分類的效果衡量 52
第四章、 實驗結果與討論 54
1、 實驗資料擷取結果 54
2、 類別標記實驗結果 55
2.1 情感傾向類別標記結果 55
2.2 情感傾向標記實驗結果討論 58
2.3 關注議題類別標記結果 60
2.4 關注議題標記實驗結果討論 67
3、 視覺化分析結果 69
3.1 對應分析結果 69
3.2 情感趨勢走向分析結果 71
3.3 視覺化分析實驗結果討論 86
4、 監督式學習實驗結果 88
4.1 情感傾向分類結果 88
4.2 關注議題分類結果 89
4.3 監督式學習實驗結果討論 91
第五章、 研究結論與建議 92
1、 結論與貢獻 92
2、 未來研究建議 95
參考文獻 96

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