臺灣博碩士論文加值系統

　　現今虛擬論壇自由且便利之發言平台，透過發言規範與論壇管理員之審核，論壇使用者即可輕易地發表各項文章，然而，因使用者逐漸增加下，多數虛擬論壇開始設有管理者用以審核違規之文章，但因大量文章注入虛擬論壇中，導致論壇管理者難以逐一審核並回請修正所有之文章，且對於發文者而言，可能於無法意識中寫入之違規字詞，於此，文章撰寫者發文後將隨即觸犯論壇規範，並需再針對違規文章進行修改，使得發文者再發文之意願逐漸低下。
　　有鑑於上述問題，本研究乃發展「以論壇文章蘊含情感為基之文章重組模式」，並劃分為「文章表達情緒判定模組」與「發文者文章語句重組模組」兩核心模組，以論壇文章為分析之基礎，發展一套適用於論壇之方法論。於前者中乃以文章代表事件分析、情緒詞彙隸屬係數建立、相似語句分析、情緒機率與穩定值解析等技術，以推論文章之情緒類別；於後者中，本研究則以整合語意相似分析、評閱分數分析、候選填充句建立、以及多重組合句建立等技術，於最終將可重組文章之語句架構。
　　為確認本研究方法論於實務應用之可行性，本研究乃建構一套以網際網路環境為基礎之以論壇文章蘊含情感為基之文章重組系統。此外，為驗證本系統之績效，本研究乃以「奇摩新聞」與比較研究所採用之「痞客邦」，作為「文章表達情緒判定模組」之驗證資料來源，並以「Mobile01論壇」作為「發文者文章語句重組模組」之驗證資料來源，並於最終績效驗證指標得知，於「文章表達情緒判定模組」中與其他研究比較後三項指標皆高於近10%之水準，於系統自我績效評估部分三項指標皆獲致80%之水準；於「發文者文章語句重組模組」中，可獲致整體平均召回率78.51%、正確率78.58%、F值為77.53%之績效水平。
　　綜合言之，本研究乃分析可能具有偏激詞語之文章並針對此類型文重組語句，就論壇管理者而言，可針對特定情緒之文章可快速取得違規文章並將重組違規之語句；就文章撰寫者言之，可自動將違規文章之語句進行修正，以節省修改違規文章之時間與人力，並可立即得知文章違規之可能性。因此，期望透過本研究所發展之模式，協助論壇管理者快速過濾違規之文章，並避免文章撰寫者撰寫之文章觸犯論壇規範。

　　Most articles are audited by forum administrator before article posting and sharing in order to improve the quality of the virtual forum articles in virtual forum. However, the users in forum gradually increases, the forum administrator difficultly individually reviews illegal articles and requests users modify their articles. For article posters, the illegal words or sentences may be written unconsciously result in violating community standards; therefore, it may take more time to modify for the illegal articles. This paper constructs a Forum Article Paraphrasing Model based on article implied emotions to analyze the article with extreme words and to rewrite and paraphrase its content to promote the its value. Finally, in order to demonstrate applicability of the proposed methodology, a web-based system is also established based on the proposed model. Furthermore, a real-world case is applied to evaluate the proposed model and system.

誌謝 V
摘要 VI
ABSTRACT VII
目錄 VIII
圖目錄 X
表目錄 XV
第一章 、研究背景 1
1.1研究動機與目的 1
1.2研究步驟 7
1.3研究範圍與限制 9
第二章 、文獻回顧 11
2.1研究定位 11
2.2虛擬社群之管理機制探討 12
2.2.1影響社群文章共享因素 12
2.2.2社群文章審核機制 16
2.3社群文章資料探勘 20
2.3.1社群文章語句重組技術 20
2.3.2社群文章評級技術 24
2.4文章撰寫者行為探討 29
2.4.1文章撰寫者情感分類 29
2.4.2文章撰寫者寫作習慣分析 34
2.4.2文章閱讀者閱讀感受分析 39
2.5小結 44
第三章 、以論壇文章蘊含情緒為基之文章重組模式 49
3.1文章表達情緒判定模組 49
3.2發文者文章語句重組模組 59
第四章 、系統架構 69
4.1以論壇文章蘊含情緒為基之文章重組系統之核心架構 69
4.2系統功能架構 71
4.3資料模式定義 74
4.4系統流程 78
4.4.1系統功能流程 78
4.4.2系統資料流程 83
4.5系統開發工具 84
第五章 、系統實作與案例分析 87
5.1系統案例之應用流程 87
5.2系統案例驗證與評估 102
5.2.1文章表達情緒判定之驗證 108
5.2.2發文者文章語句重組之驗證 116
5.3小結 129
第六章、結論與未來發展 130
6.1論文總結 130
6.2未來展望 133
參考文獻 134
附錄(1)、系統功能操作說明 143
附錄(2)、模組步驟運算與應用說明 184

圖目錄
圖1.1、範例文章 2
圖1.2、文章撰寫者所提之觀點 2
圖1.3、文章所違反之規範 3
圖1.4、Mobile01中具有互動之違規文章 4
圖1.5、卡提諾論壇中具有互動之違規文章 4
圖1.6、論壇管理機制審核文章之既有模式 5
圖1.7、論壇管理機制審核文章之期望模式 6
圖1.8、研究架構 9
圖2.1、研究定位圖 12
圖3.1、以論壇文章蘊含情緒為基之文章重組模式之流程架構 49
圖3.2、文章表達情緒判定示意圖 51
圖3.3、發文者文章語句重組示意圖 60
圖4.1、以論壇文章蘊含情緒為基之文章重組系統之流程架構 70
圖4.2、以論壇文章蘊含情緒為基之文章重組系統之功能架構 72
圖4.4、以論壇文章蘊含情緒為基之文章重組系統之資料關聯 77
圖4.5、論壇文章維護模組之功能流程 79
圖4.6、情緒詞彙維護模組之功能流程 80
圖4.7、文章表達情緒判定模組之功能流程 81
圖4.8、發文者文章語句重組模組之功能流程 83
圖4.9、系統資料流程 84
圖5.1、以論壇文章蘊含情感為基之文章重組系統之應用流程 88
圖5.2、Mobile01論壇之樣本資料(1) 89
圖5.3、Mobile01論壇之樣本資料(2) 89
圖5.4、Mobile01論壇之樣本資料(3) 89
圖5.5、門檻值修改介面 90
圖5.6、門檻值修改成功提示 90
圖5.7、論壇文章上傳輸入介面 91
圖5.8、論壇文章上傳成功介面 91
圖5.9、各情緒詞彙之關係係數解析結果 92
圖5.10、各情緒詞彙與類別隸屬係數解析結果 93
圖5.11、候選事件標題加權分數解析介面 94
圖5.12、候選事件前後文配對分數解析介面 94
圖5.13、候選事件詞性相關性分數解析介面 94
圖5.14、文章代表事件解析介面 95
圖5.15、取得文章語句中具有代表事件之語句 96
圖5.16、取得文章代表語句之相似語句 96
圖5.17、取得相似語句之情緒機率值 96
圖5.18、取得文章表達之情緒類別 97
圖5.19、文章初始分數計算介面 98
圖5.20、文章評閱分數計算介面 98
圖5.21、取得文章各語句之關聯程度介面 99
圖5.22、去除語意相似之語句介面 99
圖5.23、取得代表及重要語句之候選填充句介面 100
圖5.24、取得候選填充句之多重組合句 100
圖5.25、取得文章多重語句架構-第1組 101
圖5.26、取得文章多重語句架構-第2組 101
圖5.27、取得評閱分數最高之文章組合 102
圖5.28、評閱分數最高之文章資訊與內容 102
圖5.29、系統驗證與評估之架構 103
圖5.30、痞客邦部落格之文章資料 107
圖5.31、奇摩新聞之新聞文章 107
圖5.32、Mobile01論壇之文章資料 108
圖5.33、本研究與Tung與Lu（2012）推論數據之比較 113
圖5.34、第一階段情緒類別推論三項指標推論之分佈趨勢 114
圖5.35、系統自我績效評估-各驗證週期推論績效之分佈趨勢 115
圖5.36、發文者文章語句重組推論召回率 125
圖5.37、發文者文章語句重組推論正確率 126
圖5.38、發文者文章語句重組推論F值 126
圖5.39、發文者文章語句重組推論-各驗證週期推論績效之分佈趨勢 128
圖A.1、論壇文章資訊上傳 144
圖A.2、論壇文章資訊擷取 145
圖A.3、論壇文章新增成功 145
圖A.4、論壇文章查詢條件 146
圖A.5、論壇文章查詢結果 147
圖A.6、論壇文章詳細資訊 147
圖A.7、論壇文章資料查詢 149
圖A.8、選取欲修改之論壇文章 149
圖A.9、修改目標文章內容 150
圖A.10、論壇文章修改成功介面 150
圖A.11、輸入論壇文章查詢條件 151
圖A.12、論壇文章查詢結果 152
圖A.13、論壇文章確認刪除介面 152
圖A.14、論壇文章資料刪除成功介面 153
圖B.1、情緒詞彙新增介面 154
圖B.2、情緒詞彙新增狀況 154
圖B.3、情緒詞彙查詢介面 155
圖B.4、情緒詞彙查詢結果 156
圖B.5、情緒詞彙之類別隸屬係數結果 156
圖B.6、情緒詞彙查詢介面 157
圖B.7、情緒詞彙修改介面 158
圖B.8、情緒詞彙修改結果介面 158
圖B.9、情緒詞彙查詢介面 159
圖B.10、情緒詞彙查詢結果及詳細資料 160
圖B.11、情緒詞彙刪除結果 160
圖C.1、文章表達情緒判定模組各功能步驟運作圖 162
圖C.2、文章查詢介面 164
圖C.3、文章斷詞與語句分割介面 164
圖C.4、候選事件標題加權分數解析介面 165
圖C.5、候選事件前後文配對分數解析介面 165
圖C.6、候選事件詞性相關性分數解析介面 166
圖C.7、文章代表事件解析介面 166
圖C.8、分析訓練文章所含之情緒語句 167
圖C.9、各情緒詞彙之關係係數解析結果 168
圖C.10、各情緒詞彙與類別隸屬係數解析結果 168
圖C.11、選擇已判定代表事件之文章 170
圖C.12、取得文章語句中具有代表事件之語句 170
圖C.13、取得文章代表語句之相似語句 171
圖C.14、取得相似語句之情緒機率值 171
圖C.15、取得文章表達之情緒類別 172
圖D.1、發文者文章語句重組模組各功能步驟運作圖 173
圖D.2、文章查詢介面 174
圖D.3、文章初始分數計算介面 175
圖D.4、文章評閱分數計算介面 175
圖D.5、取得重要語句之英文詞彙介面 177
圖D.6、取得重要語句之英文延伸定義詞彙介面 178
圖D.7、取得文章各語句之關聯程度介面 178
圖D.8、去除語意相似之語句介面 179
圖D.9、取得代表及重要語句之候選填充句介面 179
圖D.10、取得候選填充句之多重組合句 180
圖D.11、取得文章多重語句架構-第1組 181
圖D.12、取得文章多重語句架構-第2組 182
圖D.13、取得評閱分數最高之文章組合 182
圖D.14、評閱分數最高之文章資訊與內容 183
圖E.1、文章表達情緒判定模組之流程步驟 184
圖E.2、標題「台灣與FBI犯罪心理官」之實際文章 185
圖E.3、標題「唉~無奈」之實際文章 186
圖E.4、標題「女友吵架…」之實際文章 187
圖E.5、發文者文章語句重組模組之流程步驟 190
圖E.6、標題「台灣這些球員…」之實際文章 190
圖E.7、標題「下雪天開車，可怕的經驗」之實際文章 193

表目錄
表2.1、影響社群文章共享因素文獻彙整表 16
表2.2、社群文章審核機制文獻彙整表 20
表2.3、社群文章語句重組技術文獻彙整表 24
表2.4、社群文章評級分析文獻彙整表 29
表2.5、文章撰寫者情感分類文獻彙整表 34
表2.6、文章撰寫者寫作習慣分析文獻彙整表 39
表2.7、文章閱讀者閱讀感受分析文獻彙整表 43
表2.8、本研究與過去文獻差異彙整表 45
表3.1、方言情緒字詞之比較 50
表3.2、訓練文章所有候選事件詞性種類之統計 55
表3.3、情緒類別所對應之情緒詞彙 56
表3.4、情緒詞彙與各情緒類別之隸屬係數 57
表3.5、相似語句與情緒類別之機率值 58
表3.6、代表語句與情緒類別之穩定值 59
表3.7、論壇文章架構與評閱分數彙整表 67
表3.8、參考文獻延伸與本研究發展彙整表 68
表5.1、各大部落格之質化比較（數據為2010年7與8月之統計結果） 104
表5.2、各大新聞網站之質化比較（統計數據為2013年5月） 105
表5.3、各大科技與3C產品討論論壇之質化比較 106
表5.4、痞客邦實際測試文章（以5份為例） 109
表5.5、奇摩新聞之20份實際測試文章 110
表5.6、系統自我績效評估之情緒類別與情緒代號 111
表5.7、系統自我績效評估之第一階段驗證結果（共200筆訓練文章） 114
表5.8、系統自我績效評估之推論結果彙整 115
表5.9、三項驗證指標成長率之彙整表 116
表5.10、測試文章編號1之偏激語句判斷問卷 118
表5.11、測試文章編號1之偏激語句判斷問卷(續) 118
表5.12、20份測試文章之實際內容與偏激語句數 119
表5.13、20份測試文章之實際內容與偏激語句數(續) 120
表5.14、30位受測者對於20份測試文章之偏激語句數調查結果 121
表5.15、30位受測者對於20份測試文章之偏激語句數調查結果(續) 122
表5.16、30位受測者主觀差異性調查結果 123
表5.17、系統自我績效評估之第一階段驗證結果（共200筆訓練文章） 127
表5.18、發文者文章語句重組之推論結果彙整 127
表5.19、三項驗證指標成長率之彙整表 129
表E.1、「治安史」事件之論壇文章資訊 185
表E.2、訓練文章資訊 188
表E.3、目標語句與相似語句之詞彙頻率表 189
表E.4、論壇之文章資訊 191
表E.5、訓練文章資訊 191
表E.6、論壇文章資訊 193

1.Afraz, Z. S., Muhammad, A. and Ana M. M. E., 2010, “Lexicon based sentiment analysis of Urdu text using SentiUnits,” Advances in Artificial Intelligence, Vol. 6437, pp. 32-43.
2.Alavi, M. and Leidner, D. E., 2012, “Review knowledge management and knowledge management systems Conceptual foundations and research issues,” MIS Quarterly, Vol. 25, No. 1, pp. 107-136.
3.Bai, X., 2011, “Predicting consumer sentiments from online text,” Decision Support Systems, Vol. 50, No. 4, pp. 732-742.
4.Chan, S. W. K., 2006, “Beyond keyword and cue-phrase matching: A sentence-based abstraction technique for information extraction,” Decision Support Systems, Vol. 42, No. 2, pp. 759-777.
5.Chen, Y. Y., Liu, C. L., Chang, T. H. and Lee, C. H., 2010, “An unsupervised automated essay scoring system,” Intelligent Systems, Vol. 25, No. 5, pp. 61-67.
6.Danushka, B., Naoali, O. and Mitsuru, I., 2012, “A preference learning approach to sentence ordering for multi-document summarization,” Information Sciences, Vol. 217, pp. 78-95.
7.Das, Dipankar. and Bandyopadhyay, S., 2010, “Sentence level emotion tagging on blog and news corpora,” Advances in Pattern Recognition, Vol. 6256, pp. 332-341.
8.David, D. R., Fernando, M., Ramon, L. H., Stephan, M., Ricardo, G., Cerferino, M. and Francisco, D. P., 2011, “Conflict and cognitive control during sentence comprehension Recruitment of a frontal network during the processing of Spanish object-first sentences,” Neuropsychologia, Vol. 49, No. 3, pp. 382-391.
9.Eikvil, L., 1999, “Information extraction from world wide web: A survey,” Norwegian Computing Center, No. 945.
10.Ercan, G. and Cicekli, I., 2007, “Using lexical chains for keyword extraction,” Information Processing and Management, Vol. 43, No. 6, pp. 1705-1714.
11.Fan, N., Cai, W. D., Zhao, Y. and Li, H. X., 2009, “Extraction of sentiment topic sentences of Chinese texts,” Journal of Computer Applications, Vol. 29, No. 4, pp. 1171-1174.
12.Fang, Y. H. and Chiu, C. M., 2010, “In justice we trust: Exploring knowledge sharing continuance intentions in virtual communities of practice,” Computers in Human Behavior, Vol. 26, No. 2, pp. 235-246.
13.Fu, X., Liu, G., Guo, Y. and Wang, Z., 2013, “Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon,” Knowledge-Based Systems, Vol. 37, pp 186-195.
14.Ge, S. L. and Chen, X. X., 2009, “Cluster analysis of college English writing in automated essay scoring,” Computer Engineering and Applications, Vol. 45, No. 6, pp. 145-148.
15.Gregory, P. S. and Daniel, N. A., 2007, “Emotion intensity and categorization ratings for emotional and nonemotional words,” Cognition and Emotion, Vol. 22, No. 1, pp. 114-133.
16.Gu, Y. and Grossman, R. L., 2010, “Sector: A high performance wide area community data storage and sharing system,” Future Generation Computer Systems, Vol. 26, No. 5, pp. 720-728.
17.Guido, B. and Leendert, V. D. T., 2006, “Security policies for sharing knowledge in virtual communities,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 36, No. 3, pp. 439-450.
18.Hamouda, A., Marei, M. and Rohaim, M., 2011, “Building machine learning based senti-word lexicon for sentiment analysis,” Journal of Advances in Information Technology, Vol. 2, No. 4, pp. 199-203.
19.Hao, X. Y., Li, J. H., You, L. P. and Liu, K. Y., 2007, “A research on building of Chinese reading comprehension corpus,” Journal of Chinese Information Processing, Vol. 21, No. 6, pp. 29-35.
20.Hollink, L., Schreiber, A. T., Wielinga, B. J. and Worring, M., 2004, “Classification of user image descriptions,” International Journal of Human-Computer Studies, Vol. 61, No. 5, pp. 601-626.
21.Hsu, C. L. and Lin, C. C., 2007, “Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation,” Information & Management, Vol. 45, No. 1, pp. 65-74.
22.Huang, J., Tian, S. W., Yu, L. and Feng, G. J., 2012, “Sentence sentiment analysis based on Uyghur sentiment word,” Computer Engineering, Vol. 38, No. 9, pp. 182-185.
23.Ichifuji, Y., Konno S. and Sone, H., 2010, “An advisory method for BBS users and evaluation of BBS comments,” Procedia Social and Behavioral Sciences, Vol. 2, No. 1, pp. 218-224.
24.Jiang, H., Huang, G. Q. and Liu, J. G., 2011, “The research on CET automated essay scoring based on data mining,” Advances in Computer Science and Education Applications, Vol. 202, pp. 100-105.
25.Jonathan, K. and Yiu, K. N., 2006, “Using word clusters to detect similar web documents,” Knowledge Science, Engineering and Management, Vol. 4092, pp. 215-228.
26.Jung, H., Yi, E., Kim, D. and Lee, G. G., 2005, “Information extraction with automatic knowledge expansion,” Information Processing and Management, Vol. 41, No. 2, pp. 217-242.
27.Kaila, S., Huang, H., Wu, X. and Zhang, S., 2006, “A logical framework for identifying quality knowledge from different data sources,” Decision Support Systems, Vol. 42, No. 3, pp. 1673-1683.
28.Kakkonen, T., Myller, N., Sutinen, E. and Timonen, J., 2008, “Comparison of dimension reduction methods for automated essay grading,” Educational Technology & Society, Vol. 11, No. 3, pp. 275-288.
29.Ko, Y. and Seo, J., 2008, “An effective sentence-extraction technique using contextual information and statistical approaches for text summarization,” Pattern Recognition Letters, Vol. 29, No. 9, pp. 1366-1371.
30.Kuo, Y. H. and Huang, H. H., 2007, “Automatic extraction of key sentences via word sense identification for Chinese text summarization,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 11, No. 4, pp. 416-422.
31.Lai, H, M. and Chen, T. T., 2014, “Knowledge sharing in interest online communities: A comparison of posters and lurkers,” Computers in Human Behavior, Vol. 35, pp. 295-306.
32.Lai, L. F., 2007, “A knowledge engineering approach to knowledge management,” Information Sciences, Vol. 177. No. 19. pp. 4072-4094.
33.Lee, K. J., Choi, Y. S. and Kim, J. E., 2011, “Building an automated English sentence evaluation system for students learning English as a second language,” Computer Speech and Language, Vol. 25, No, 2 pp. 246-260.
34.Li, C. L., Zhu, Y. H. and Xu, Y. Q., 2011, “Research of attribute word extraction method in Chinese product comment,” Computer Engineering, Vol. 37, No. 12, pp. 26-29.
35.Li, G. and Liu, F., 2012, “Application of a clustering method on sentiment analysis,” Journal of Information Science, Vol. 38, No. 2, pp. 127-139.
36.Li, N. and Wu, D. D., 2010, “Using text mining and sentiment analysis for online forums hotspot detection and forecast,” Decision Support System, Vol. 48, No. 2, pp. 354-368.
37.Li, S., Ye, Q., Li, Y. j. and Luo, S. Q., 2008, “Information inference based sentiment classification for online news comments,” Journal of Chinese Information Processing, Vol. 23, No. 5, pp. 75-79.
38.Li, Y. H., David, M., Zuhair, A. B., James, D. O. and Keeley, C., 2006, “Sentence similarity based on semantic nets and corpus statistics,” Transactions on Knowledge and Data Engineering, Vol. 18, No, 8 pp. 1138-1150.
39.Li, Y. M., Liao, T. F. and Lai, C. Y., 2012, “A social recommender mechanism for improving knowledge sharing in online forums,” Information Processing and Management, Vol. 48, No. 5, pp. 978-994.
40.Liao, C. H., Kuo, B. C. and Pai, K. C., 2012, “Effectiveness of automated Chinese sentence scoring with latent semantic analysis,” The Turkish Online Journal of Educational Technology, Vol. 11, No, 2 pp. 80-87.
41.Lin, F. R. and Liang, C. H., 2008, “Storyline-based summarization for news topic retrospection,” Decision Support Systems, Vol. 45, No. 3, pp. 473-490.
42.Lin, Y., Ye, Q., Li, J., Zhang, Z. and Wang, T., 2011, “Snippet-based unsupervised approach for sentiment classification of Chinese online reviews,” International Journal of Information Technology & Decision Making, Vol. 10, No. 6, pp. 1097-1110.
43.Liu, J., Wang, B., Lu, H. and Ma, S., 2008, “A graph-based image annotation framework,” Pattern Recognition Letters, Vol. 29, No. 4, pp. 407-415.
44.Liu, W. P., Zhu, Y. H., Li, C. L., Xiang, H. Z. and Wen, Z. Q., 2009, “Research on building Chinese basic semantic lexicon,” Journal of Computer Application, Vol. 29, No. 10, pp. 2875-2877.
45.Liu, W., Yan, H. L., Xiao, J. G. and Zeng, J. X., 2010, “Solution for automatic web review extraction,” Journal of Software, Vol. 21, No. 12, pp. 3220-3236.
46.Liu, Z, M. and Liu, L., 2012, “Empirical study of sentiment classification for Chinese microblog based on machine learning,” Computer Engineering and Applications, Vol. 40, No. 1, pp. 1-4.
47.Lu, J., Liang, Y. H., Lu, Y. Q., Li, B. and Yao, J. M., 2009, “The application of classifier combining in automated essay scoring,” Micro Electronics & Computer, Vol. 26, No. 10, pp. 69-73.
48.Matsubara, D., Miki, K., Inouchi, H. and Hoshi, T., 2006, “File management using virtual directory architecture for central managed P2P information sharing system (NRBS),” Information and Media Technologies, Vol. 1, No. 1 pp.514-523.
49.Matsumoto, K., Konishi, Y., Sayama, H. and Ren, F., 2011, “Analysis of Wakamono Kotoba emotion corpus and its application in emotion estimation,” International Journal of Advanced Intelligence, Vol. 3, No. 1, pp. 1-24.
50.Miao, Y., Su, J., Liu, S. and Zhang, J., 2012, “Bootstrapping-based method for chinese sentiment lexicon construction,” International Conference on Information Engineering Lecture Notes in Information Technology, Vol. 25, pp. 248-253.
51.Oh, H., Fiorito, S. S., Cho, H. and Hofacker, C. F., 2008, “Effects of design factors on store image and expectation of merchandise quality in web-based stores,” Journal of Retailing and Consumer Services, Vol. 15, No. 4, pp. 237-249.
52.Okamoto, J. and Ishizaki, S., 2011, “Important sentence extraction using contextual semantic network,” Procedia-Social and Behavioral Sciences, Vol. 27, pp 86-94.
53.Pang, H. J., 2010, “Text sentiment analysis-oriented commodity review detection,” Journal of Computer Applications, Vol. 32, No. 7, pp. 2038-2041.
54.Peng, H., Shi, Z. Z., Qiu, L. L. and Chang, L., 2008, “Matching algorithm of semantic web service based on similarity of ontology concepts,” Computer Engineering, Vol. 34, No. 15, pp. 51-53.
55.Peng, J., Yang D. Q., Tang, S. W., Fu, J. and Jiang, H. K., 2007, “A novel text clustering algorithm based on inner product space model of semantic,” Chinese Journal of Computers, Vol. 30, No. 8, pp. 1354-1363.
56.Pérez, M. S., Sánchez, A., Robles, V. and Herrero, P., 2007, “Design and implementation of a data mining grid-aware architecture,” Future Generation Computer Systems, Vol. 23, No. 1, pp. 42-47.
57.Rorissa, A., 2008, “User-generated descriptions of individual images versus labels of groups of images: A comparison using basic level theory,” Information Processing and Management, Vol. 44, No. 5, pp. 1741-1753.
58.Teo, H., Chan, H. C., Wei, K. K. and Zhang, Z., 2003, “Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities,” International Journal of Human-Computer Studies, Vol. 59, No. 5, pp. 671-697.
59.Tokuhisa, R., Inui, K., Liu, S. and Matsumoto, Y., 2008, “Emotion classification using massive examples extracted from the web,” Proceedings of the 22nd International Conference on Computational Linguistics, pp. 881-888.
60.Tractinsky, N., Cokhavi, A., Kirschenbaum, M. and Sharfi, T., 2006, “Evaluating the consistency of immediate aesthetic perceptions of web pages,” International Journal of Human-Computer Studies, Vol. 64, No. 11, pp. 1071-1083.
61.Tung, C. M. and Lu, W. H., 2012, “Predict depression tendency of web posts using negative emotion evaluation model,” In ACM SIGKDD Workshop on Health Informatics.
62.Wang, C. Y., Yang, H. Y. and Chou S. C. T., 2008, “Using peer-to-peer technology for knowledge sharing in communities of practices,” Decision Support Systems, Vol. 45, No. 3, pp. 528-540.
63.Wang, S. and Noe, R. A., 2010, “Knowledge sharing: A review and directions for future research,” Human Resource Management Review, Vol. 20, No. 2, pp. 115-131.
64.Wang, S. G. and Li, W., 2010, “Research on sentiment classification problem of sino-Japanese relations forum,” Computer Engineering and Applications, Vol. 43, No. 32, pp. 174-177.
65.Wang, S. G., Li, D. Y., Wei, Y. J. and Song, X. L., 2009, “A synonyms based word sentiment orientation discriminating,” Journal of Chinese Information Processing, Vol. 23, No. 5, pp. 68-74.
66.Wang, X. D., Liu, Q. and Tao, X. J., 2010, “Sentiment ontology construction and text orientation analysis,” Computer Engineering and Applications, Vol. 46, No. 30, pp. 117-120.
67.Wang, X. D., Liu, Q. and Zhang, Z., 2011, “Topic semantic orientation compute based on sentiment words ontology,” Computer Engineering and Applications, Vol. 47, No. 27, pp. 147-151.
68.Wang, Z. H. and Jiang, W., 2012, “Online reviews sentiment analysis model based on rough sets,” Computer Engineering, Vol. 38, No. 16, pp. 1-4.
69.Win, K. T. and Zhang, M., 2006, “Enhancing information quality through a semantic approach in health information retrieval,” International Journal of Electronic Business Management, Vol. 4, No. 1, pp. 8-15.
70.Xie, L., Zhou, M. and Sun, M., 2012, “Hierarchical structure based Hybrid approach to sentiment analysis of Chinese micro blog and its feature extraction,” Journal of Chinese Information Processing, Vol. 26, No. 1 pp. 73-83.
71.Xu, C., Wang, M., He, T. T. and Zhang, Y., 2008, “Automatic summarization method based on extracting sentences from local topics,” Computer Engineering, Vol. 34, No. 22, pp. 49-51.
72.Yang, F, Peng, Q. K. and Xu, T., 2010, “Sentiment classification for online comments based on random network theory,” Acta Autimatica Sinica, Vol. 36, No. 6, pp. 837-844.
73.Yang, J., Peng, S. Y. and Hou, M., 2011, “Recognizing sentiment polarity in Chinese reviews based on top sentiment sentences,” Application Research of Computers, Vol. 28, No. 2, pp. 569-572.
74.Yin, C. X. and Peng, Q. K., 2012, “Identifying word sentiment orientation for free comments via complex network,” Acta Automatica Sinica, Vol. 38, No. 3, pp. 389-390.
75.Yu, Z. T., Fan, X. Z., Guo, J. Y. and Geng, Z. M., 2006, “Answer extraction for Chinese question-answer system based on latent semantic analysis,” Chinese journal of computers, Vol. 29, No. 10, pp. 1888-1893.
76.Zan, H. Y., Zuo, W. S., Zhang, K. L. and Wu, Y. F., 2011, “Sentiment analysis based on rules and statistics,” Computer Engineering and Science, Vol. 33, No. 5, pp. 146-150.
77.Zhan, P., Zhao, Z. W. and Zhuo, J. W., 2011, “Method of sentence semantic orientation distinction based on semantic weighted sentiment word,” Computer Engineering and Applications, Vol. 47, No. 35, pp. 161-164.
78.Zhang, C., Zeng, D., Li, J., Wang, F. Y. and Zuo, W., 2009, “Sentiment analysis of Chinese documents: From sentence to document level,” Journal of the American Society for Information Science and Technology, Vol. 60, No. 12, pp. 2474-2487.
79.Zhang, J., 2009, “An overview of factors influencing knowledge sharing in virtual communities,” Journal of Modern Information, Vol. 29, No. 7, pp. 222-225.
80.Zhang, W., Li, P. and Zhu, Q., 2011, “Research on tree kernel-based sentiment classification in English sentences,” Computer Applications and Software, Vol. 28, No. 4, pp. 30-33.
81.Zhang, Y. X., Zhang, M. and Deng, Z. H., 2009, “Feature-driven summarization of customer reviews,” Journal of Computer Research and Development, Vol. 46, No. 2, pp. 520-525.
82.Zhao, Y. Y., Bing, Q. and Ting, L., 2010 “Integrating intra- and inter-document evidences for improving sentence sentiment classification,” Acta Automatica Sinica, Vol. 36, No. 10, pp. 1417-1425.
83.Zhou, J., Lin, C. and Li, B. C., 2010, “Research of sentiment classification for Netnews comments by machine learning,” Journal of Computer Application, Vol. 30, No. 4, pp. 1011-1014.
84.Zhou, L., Tao, Y., Cimino, J. J., Chen, E. S., Liu, H., Lussier, Y. A., Hripcsak, G. and Friedman, C., 2006, “Terminology model discovery using natural language processing and visualization techniques,” Journal of Biomedical Informatics, Vol. 39, No. 6, pp. 626-636.
85.李旻芳，2010，「不同質地之組合對情感感受之影響-以木皮與不鏽鋼質地為例」，碩士論文（指導教授：李文淵），大同大學工業設計研究所。
86.高郁倫，2010，「人格特質對於網路社群知識分享及創造影響情形之研究-以程式設計論壇為例」，碩士論文（指導教授：謝佩璇），國立成功大學資訊管理研究所。
87.創市際市場研究顧問，「創市際月刊報告書2010年7、8與10月」，http://news.ixresearch.com/?page_id=1534
88.創市際市場研究顧問，「創市際月刊報告書2013年5月」，http://news.ixresearch.com/?page_id=1534
89.黃信華，2013，「以Facebook塗鴉牆文本分析情緒文字的關係」，碩士論文（指導教授：林豪鏘），國立臺南大學數位學習科技學系碩士班。
90.廖顯忠，2010，「分析隱含於部落格文章的事件、情緒與需求」，碩士論文（指導教授：盧文祥），國立成功大學資訊工程研究所。