跳到主要內容

臺灣博碩士論文加值系統

(3.236.110.106) 您好!臺灣時間:2021/07/27 20:02
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:彭志恩
研究生(外文):Jhih-en Peng
論文名稱:擷取3C產品特徵之研究
論文名稱(外文):Analysis of Extracting 3C Product Features
指導教授:王惠嘉王惠嘉引用關係
指導教授(外文):Hei-chia Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:56
中文關鍵詞:自動文件摘要產品評論語意分析文字探勘
外文關鍵詞:automatic document abstractText miningproduct opinionsemantic analysis
相關次數:
  • 被引用被引用:1
  • 點閱點閱:348
  • 評分評分:
  • 下載下載:84
  • 收藏至我的研究室書目清單書目收藏:1
隨著網際網路的蓬勃發展,顧客對於產品的評價的方式也有了改變,從口耳相傳到如今在各種網路平台上發表評論文章,例如:epinion.com、amazon.com…等。產品評論文章包含了顧客所關心的產品特徵以及顧客使用產品經驗的資訊,這些資訊對於潛在顧客而言,能夠將評論視為購買產品決策的重要因素之一;對於產品製造商而言,則能夠根據產品評論所提供的資訊加以改良產品設計、制訂市場行銷策略並且對於特定的顧客提供個人化的服務。
然而隨著使用網路人口的增加,各種產品評論文章的數量也急劇成長,造成潛在顧客必須花費大量的精力與時間,尋找與產品相關的評論文章並一一閱讀,過濾出有用的資訊。儘管藉由入口網站的搜尋引擎,可將這些相關的評論文章集中在一定的範圍之內,但其搜尋結果的數量仍可能達到上千筆或上萬筆。若是潛在顧客想對多款產品進行全面性的比較,則必須要投入更多的時間和精力在搜尋及閱讀相關的文章,這樣的現象造成潛在顧客很難去一篇一篇地閱讀評論文章以及整理產品各種特徵的正面或負面評價,來幫助自己決定是否購買此產品;此外,也使得產品製造商難以追蹤以及管理產品的評價。
為使能更方便了解大量評價文件,本文將提出自動化處理的方法。但在一篇產品評論中,很重要的資訊就是句子中對於產品特徵有主觀性敘述的部份,然而傳統的文件摘要技術對文件中所有字同視為一樣重要,這會導致對於產品評論文章只能夠產生一般性的描述,卻忽略了有關於產品特徵的資訊。因此,本研究將提出一套有效的產品特徵擷取技術,用以提升產品評論自動摘要的效能。實驗結果得知,引用資源豐富的Wikipedia能夠擷取出更多使用者討論的產品評論。
With the rapid development of Internet, it has changed about the way customers evaluating product, from talking with others to issuing articles on various websites like epinion.com、amazon.com etc. Product opinion articles include product features customers caring and information of experiences about customers using products. The information for potential customers can be seen a significant factor of product purchasing decisions, and it can help product manufacturers to improve product designs, establish marketing strategies and provide service for specific customers.
A large amount of various product opinion articles are growing rapidly, which cause that potential customers have to cost a lot of energy and time to search related opinion articles and read them one by one in order to filter useful information. This phenomenon causes that potential customers is hard to read product opinion articles themselves and to sort positive or negative opinions of every product features in order to help themselves to purchase this product or not. Besides, it causes that product manufacturers are hard to trace and management opinions of products.
In order to understand a lot of opinion articles conveniently, we will propose the way of automatic treatment. The important information in a product opinion articles is the subjective statement of product features in a sentence, but traditional technology of document abstracts takes all words the same weight. Therefore, we will propose an effective technology of extracting product features to promote the efficiency of automatically producing opinion abstracts.
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
1. 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究範圍與限制 3
1.4 研究流程 3
1.5 論文架構 4
2. 文獻探討 6
2.1 文件摘要處理 6
2.1.1 單一文件的摘要方法 7
2.1.2 多重文件的摘要方法 7
2.2 產品特徵擷取 8
2.2.1 非監督式產品特徵擷取 8
2.2.2 監督式產品特徵擷取 9
2.3 基因演算法 10
2.4 維基百科 15
3. 研究方法 18
3.1 研究架構 18
3.2 前處理 (Preprocessing) 20
3.3 句子分數計算(Score Sentences) 21
3.4 產品特徵挑選 (Product Feature Selection) 25
3.5 小節 31
4. 系統建置與驗證 32
4.1 系統建置 32
4.1.1 實作環境 32
4.1.2 使用套件及模組 32
4.1.3 系統處理流程 33
4.2 實驗方法 35
4.2.1 資料來源 35
4.2.2 評估指標 35
4.3 實驗結果與分析 36
4.4 系統畫面範例 45
5. 結論及未來研究方向 47
5.1 研究成果 47
5.2 後續研究 50
參考文獻 52
附錄 55
��英文文獻
Bai, X., Padman, R., & Airoldi, E. (2005). On learning parsimonious models for extracting consumer opinions. Paper presented at the In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Los Alamitos.
Bruce, R. F., & Wiebe, J. M. (1999). Recognizing Subjectivity: A Case Study on Manual Tagging. Natural Language Engineering, 5(2), 187-205.
Cardie, C., Wiebe, J., Wilson, T., & Litman, D. (2003). Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering. Paper presented at the Proceedings of the AAAI Spring Symposium on New Directions in Question Answering.
Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Paper presented at the In Proceedings of the 12th International World Wide Web Conference, New York, NY.
Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM, 16(2), 264-285.
Fattah, M. A., & Ren, F. (2009). GA, MR, FFNN, PNN and GMM based models for automatic text summarization. Computer Speech & Language, 23(1), 126-144.
Fum, D., Guida, G., & Tasso, C. (1985). Evaluating importance: A step towards text summarization. Paper presented at the In Proceedings of the 9th international joint conference on artificial intelligence, Los Angeles, CA, USA.
Holland, J. H. (1975). Adaptation in Natural and Artificial systems. Ann Arbor, MI: The University of Michigan Press.
Hovy, E., & Lin, C. (1997). Automated text summarization in SUMMARIST. Paper presented at the In Proceedings of the ACL97/EACL97 workshop on intelligent scalable text summarization, Madrid, Spain.
Hu, M., & Liu, B. (2004). Mining and Summarizing Customer Reviews. Paper presented at the Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Kauchak, D., & Chen, F. R. (2005, June 25-30). Feature-Based segmentation of narrative documents. Paper presented at the Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing, Ann Arbor, MI.
Kim, S.-M., & Hovy, E. (2004). Determining the sentiment of opinions. Paper presented at the Proceedings of the 20th International Conference on Computational Linguistics, Morristown, NJ.
Kupiec, J., Pedersen, J. O., & Chen, F. (1995). A trainable document summarizer. Paper presented at the Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval Seattle, Washington, United States
Liu, B., Hu, M., & Cheng, J. (2005). Opinion Observer: Analyzing and comparing opinions on the Web. Paper presented at the In Proceedings of the 14th International World Wide Web Conference, New York, NY.
Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165.
McKeown, K., & Radev, D. R. (1995). Generating summaries of multiple news articles. Paper presented at the In Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval, Seattle, WA, USA.
Morinaga, S., Ya Yamanishi, K., Tateishi, K., & Fukushima, T. (2002). Mining Product Reputations on the Web. Paper presented at the In Proceedings of KDD-2002.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Paper presented at the In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Morristown, NJ.
Popescu, A.-M., & Etzioni, O. (2005). Extracting product features and opinions from reviews. Paper presented at the Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada.
Radev, D. R., Hovy, E., & McKeown, K. (2002). Introduction to the special issue on summarization. Computational Linguistics, 28(4), 399-408.
Radev, D. R., Jing, H., & Budzikowska, M. (2004). Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. Information Processing and Management, 40(6), 919-938.
Reimer, U., & Hahn, U. (1988). Text condensation as knowledge base abstraction. Paper presented at the In Proceedings of the 4th conference on artificial intelligence applications, San Diego, CA, USA.
Riloff, E., & Wiebe, J. (2003). Learning extraction patterns for subjective expressions. Paper presented at the In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Morristown, NJ.
Riloff, E., Wiebe, J., & Wilson, T. (2003). Learning subjective nouns using extraction pattern bootstrapping. Paper presented at the In Proceedings of the Seventh Conference on Natural Language Learning, Edmonton, Canada.
Salton, G., Singhal, A., Mitra, M., & Buckley, C. (1997). Automatic text structuring and summarization. Information Processing and Management, 33(2), 193-207.
Teufel, S., & Moens, M. (1997). Sentence extraction as a classification task. Paper presented at the Proceedings of ACL/EACL97-WS, Spain: Madrid.
Wang, B., & Wang, H. (2007). Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training. Web Intelligence 259-262.
Wiebe, J., Wilson, T., & Bell, M. (2001). Identify collocations for recognizing opinions. Paper presented at the In Proceedings of ACL/EACL2001 Workshop on Collocation, Morristown, NJ.
Zhan, J., Loh, H. T., & Liu, Y. (2008). Gather customer concerns from online product reviews - A text summarization approch. Expert Systems with Applications, 1-9.
Zhang, Z., Blair-Goldensohn, S., & Radev, D. R. (2002). Towards CSTenhanced summarization. Paper presented at the In Proceedings of 18th national conference on artificial intelligence, Edmonton, Alberta, Canada.

��參考網站
FASTR. from http://www.limsi.fr/Individu/jacquemi/FASTR/
Wikipedia (2008). Retrieved Nov. 20, 2008, from http://zh.wikipedia.org/w/index.php?title=%E7%B6%AD%E5%9F%BA%E7%99%BE%E7%A7%91&variant=zh-tw
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊