Finding Customer Opinions based on User-given Aspect

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姓名

黃于珊(Yu-Shan Huang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(Finding Customer Opinions based on User-given Aspect)

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摘要(中)

由於網際網路的快速發展，許多服務與購物網站累積了許多評論資訊。為了提供給消費者更多資訊使得使用者能更快速找到自己需要的評論，這些電子商務網站會利用事先定義好的產品構面將使用者的評論進行分類。以Hotels.com為例，他們事先將評論分為服務、清潔度、舒適度、整體外觀等四個構面，並給予每個構面分數。然而，依據各網站自我事先的定義，無法針對每位使用者個別的特殊需求，讓使用者找到自己所關心的相關評論資訊。因此本研究提出新的評論分析方法，能夠利用使用者能動態給予的產品構面找到相關的顧客評論。本研究包含四個主要步驟：前處理(pre-processing)、註解(annotating)、配對(matching)與排列(ranking)。而在註解過程中執行三個不同的方法找到與使用者提供的構面相關的關鍵字。而在配對過程中，利用Google與WordNet進行關鍵字的相似度計算，並利用四個不同的鏈結方法計算構面與句子的相似度。最後利用每個句子與構面的相似度排列評論句子提供給使用者。

摘要(英)

As the Internet grows day by day, customer reviews in e-commerce websites are also increasing every day. To provide users more information, these commercial websites usually would summarize users’ opinions in reviews according to some predefined aspects. For example, Hotels.com extract scores from customer reviews in four aspects, including service, cleanliness, comfort and condition. However, the weakness of this predefined analysis approach is that users cannot find opinions on the aspects that have not been considered beforehand by the websites.
To solve this problem, we propose a new approach to generate a ranking of related opinions based on user’s dynamically given aspect. The proposed approach involves 4 phase named pre-processing, annotating, matching and ranking. In the annotating phase, 3 different annotating methods are used to annotated related keywords based on user-given aspect. In the matching phase, Google similarity and WordNet similarity, are first used to compute the similarity between keywords and then the similarity between aspect and each sentences is computed by 5 different linkage methods. Finally, we rank sentences based on their similarities with aspect.

關鍵字(中)

★ 意見探勘
★ NGD
★ WordNet
★ 關鍵字註解
★ 構面分析

關鍵字(英)

★ Opinion Mining
★ NGD
★ WordNet::Similarity
★ Keyword Annotation
★ Aspect Analysis

論文目次

Abstract i
摘要 ii
致謝 iii
List of Figures vi
List of Tables vii
1.Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Our Approach 2
2.Related Work 7
2.1 Opinion Mining 7
2.1.1 Subjectivity analysis 7
2.1.2 Semantic orientation 7
2.1.3 Feature-based opinion mining 8
2.2 Our work 9
3.Research Design 11
3.1 Pre-processing 11
3.2 Annotation 13
3.2.1 Manual method 13
3.2.2 Semi-auto method 13
3.2.2.1 Keyword extraction 14
3.2.3 Fully-auto method 15
3.3 Matching 15
3.3.1 Keywords pair distance identification 16
3.3.2 Aspect-sentence distance identification 17
3.4 Output the ranking of opinions 19
4.Experiment 20
4.1 Datasets 20
4.2 Evaluation methods 20
Then, we can compute accuracy, recall and F-measure in each class. Hence, we can use them to observe the result and draw graph to compare these methods. 21
4.3 The results of experiment 21
4.3.1 The methods of annotation 23
4.3.2 The methods of computing keyword-keyword pair distance 25
4.3.3 The methods of computing aspect-sentence pair distance 26
4.4 The best recommended methods 28
5. Conclusion and Future Works 32
6. Reference 33

參考文獻

[1]Cilibrasi, R.L. and P.M.B. Vitanyi, The Google Similarity Distance. Knowledge and Data Engineering, IEEE Transactions on, 2007. 19(3): p. 370-383.
[2]Pedersen, T., S. Patwardhan, and J. Michelizzi, WordNet::Similarity: measuring the relatedness of concepts, in Demonstration Papers at HLT-NAACL 2004. 2004, Association for Computational Linguistics: Boston, Massachusetts. p. 38-41.
[3]Pang, B. and L. Lee, Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr., 2008. 2(1-2): p. 1-135.
[4]Pang, B., L. Lee, and S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, in Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10. 2002, Association for Computational Linguistics. p. 79-86.
[5]Turney, P.D., Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. 2002, Association for Computational Linguistics: Philadelphia, Pennsylvania. p. 417-424.
[6]Dave, K., S. Lawrence, and D.M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, in Proceedings of the 12th international conference on World Wide Web. 2003, ACM: Budapest, Hungary. p. 519-528.
[7]Yu, H. and V. Hatzivassiloglou, Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences, in Proceedings of the 2003 conference on Empirical methods in natural language processing. 2003, Association for Computational Linguistics. p. 129-136.
[8]Meena, A. and T.V. Prabhakar, Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis, in Proceedings of the 29th European conference on IR research. 2007, Springer-Verlag: Rome, Italy. p. 573-580.
[9]Liu, Y., et al., ARSA: a sentiment-aware model for predicting sales performance using blogs, in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 2007, ACM: Amsterdam, The Netherlands. p. 607-614.
[10]Park, S., et al., The politics of comments: predicting political orientation of news stories with commenters′ sentiment patterns, in Proceedings of the ACM 2011 conference on Computer supported cooperative work. 2011, ACM: Hangzhou, China. p. 113-122.
[11]Dergiades, T., Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy. Economics Letters, 2012. 116(3): p. 404-407.
[12]Rogers, E.M., Diffusion of innovations. 2010: Simon and Schuster.
[13]Nadeau, D., et al., Automatic dream sentiment analysis. 2006.
[14]Zhang, Y., et al., Learning from multi-topic web documents for contextual advertisement, in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 2008, ACM: Las Vegas, Nevada, USA. p. 1051-1059.
[15]Koppel, M. and I. Shtrimberg, Good news or bad news? let the market decide, in Computing attitude and affect in text: Theory and applications. 2006, Springer. p. 297-301.
[16]Chesley, P., et al., Using verbs and adjectives to automatically classify blog sentiment. Training, 2006. 580(263): p. 233.
[17]Sugumaran, V. and J.A. Gulla, Applied semantic web technologies. 2012: CRC Press. 325-344.
[18]Hu, M. and B. Liu, Mining and summarizing customer reviews, in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004, ACM: Seattle, WA, USA. p. 168-177.
[19]Hu, M. and B. Liu, Mining opinion features in customer reviews, in Proceedings of the 19th national conference on Artifical intelligence. 2004, AAAI Press: San Jose, California. p. 755-760.
[20]Zhang, L. and B. Liu, Identifying noun product features that imply opinions, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2. 2011, Association for Computational Linguistics: Portland, Oregon. p. 575-580.
[21]Jeonghee, Y., et al. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. 2003.
[22]Bourigault, D., Lexter: A terminology extraction software for knowledge acquisition from texts. KAW’95. 1995.
[23]Daille, B., Study and implementation of combined techniques for automatic extraction of terminology. The balancing act: Combining symbolic and statistical approaches to language, 1996. 1: p. 49-66.
[24]Witschel, H.F., Terminology Extraction and Automatic Indexing. Terminology and Knowledge Engineering (TKE), 2005.
[25]Justeson, J.S. and S.M. Katz, Technical terminology: some linguistic properties and an algorithm for identification in text. Natural language engineering, 1995. 1(1): p. 9-27.
[26]Popescu, A.-M. and O. Etzioni, Extracting product features and opinions from reviews, in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. 2005, Association for Computational Linguistics: Vancouver, British Columbia, Canada. p. 339-346.
[27]Qiu, G., et al., Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, 2011. 37(1): p. 9-27.
[28]You, W., et al., Customer knowledge discovery from online reviews. Electronic Markets, 2012. 22(3): p. 131-142.
[29]Hu, M. and B. Liu, Opinion Feature Extraction Using Class Sequential Rules, in Proceedings of AAAI 2006 Spring Sympoia on Computational Approaches to Analyzing Weblogs AAAI-CAAW 2006. 2006.
[30]Han, E.-H. and G. Karypis, Feature-based recommendation system, in Proceedings of the 14th ACM international conference on Information and knowledge management. 2005, ACM: Bremen, Germany. p. 446-452.
[31]Lott, B., Survey of Keyword Extraction Techniques. 2012.
[32]Robertson, S., Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 2004. 60: p. 2004.

指導教授

陳彥良(Yen-Liang Chen)

審核日期

2014-7-14

推文