國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
76
第五章 結論
本研究使用 Amazon 上數位相機 Canon PowerShot 三世代產品 SX210, SX230, SX260 之消費者評論進行面相意見探勘。不同於過去研究未考量評論會因發表先 後順序而受到影響，本研究提出之一致性改善程式，找出消費者隱而未現之意見。
最後，使用本研究提出之 GPA Matrix 輔以迴歸、決策樹分析，彙整成為 MPA Matrix 觀察消費者感受之重要面相於三個世代產品間的轉移。
然而，本研究也存在研究限制。首先，本研究為高涉入性產品，原因為消費 者在購買前會力行資料蒐集作業，降低其資訊不對稱性，因此消費者評論的重要 性顯得格外重要，然而，本研究並未討論低涉入性產品之相關研究。其次，填值 的有效性無法驗證。雖然本研究觀察消費者有隱而未現的資料，並以相關研究為 前提假設建置填值方式，卻無法取得正確答案，了解消費者實際上隱含的意見是 否如同填值後的結果，僅能以不同的至少考量數目以情境分析推測其有效性。因 此，未來研究建議能以填值後的有效性進行實驗，透過設計更精緻的填值方式，
找出消費者隱而未現的意見。以期更精準的利用資訊，在海量資料中，萃取有用 的知識，以提供廠商改善之建議。
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
77
參考文獻
[1] Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, VLDB (Vol. 1215, pp. 487-499).
[2] Airoldi, E., Bai, X., & Padman, R. (2006). Markov blankets and meta-heuristics search: Sentiment extraction from unstructured texts.
In Advances in Web Mining and Web Usage Analysis (pp. 167-187).
Springer Berlin Heidelberg.
[3] Bakos, J. Y. (1991). A strategic analysis of electronic marketplaces.
MIS quarterly, 295-310.
[4] Benamara, F., Cesarano, C., Picariello, A., Recupero, D. R., &
Subrahmanian, V. S. (2007, March). Sentiment Analysis: Adjectives and Adverbs are Better than Adjectives Alone. In ICWSM.
[5] Chen, L., Qi, L., & Wang, F. (2012). Comparison of feature-level learning methods for mining online consumer reviews. Expert Systems with Applications, 39(10), 9588-9601.
[6] Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance.Knowledge and Data Engineering, IEEE Transactions on, 19(3), 370-383.
[7] Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
78
reviews. InProceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
[8] Duric, A., & Song, F. (2011, June). Feature selection for sentiment analysis based on content and syntax models. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (pp. 96-103). Association for Computational Linguistics.
[9] Esuli, A., & Sebastiani, F. (2006, May). Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC (Vol. 6, pp. 417-422).
[10]Feiguina, O., & Lapalme, G. (2007). Query-based summarization of customer reviews. In Advances in Artificial Intelligence (pp.
452-463). Springer Berlin Heidelberg.
[11]Fu, X., Liu, G., Guo, Y., & Wang, Z. (2012). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems.
[12]Gamon, M. (2004, August). Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. InProceedings of the 20th international conference on Computational Linguistics(p. 841). Association for Computational Linguistics.
[13]Goldberg, A. B., & Zhu, X. (2006, June). Seeing stars when there aren't
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
79
many stars: graph-based semi-supervised learning for sentiment categorization. In Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing (pp. 45-52). Association for Computational Linguistics.
[14]Gu, B., Park, J., & Konana, P. (2012). Research Note—The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products. Information Systems Research, 23(1), 182-196.
[15]Hatzivassiloglou, V., & Wiebe, J. M. (2000, July). Effects of adjective orientation and gradability on sentence subjectivity.
In Proceedings of the 18th conference on Computational
linguistics-Volume 1 (pp. 299-305). Association for Computational Linguistics.
[16]Horrigan, J. A. (2008). Online shopping. Pew Internet & American Life Project Report, 36.
[17]Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. InProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177). ACM.
[18] Jopson, Barney (July 12, 2011). Amazon urges California referendum on online tax. Financial Times.
[19]Kudo, T., & Matsumoto, Y. (2004, June). A Boosting Algorithm for Classification of Semi-Structured Text. In EMNLP (Vol. 4, pp.
301-308).
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
80
[20]Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284.
[21]Mani, I., House, D., Klein, G., Hirschman, L., Firmin, T., & Sundheim, B. (1999, June). The TIPSTER SUMMAC text summarization evaluation.
In Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics (pp. 77-85). Association for Computational Linguistics.
[22]Mejova, Y. (2009). Sentiment Analysis: An Overview. Comprehensive exam paper, available on
http://www.cs.uiowa.edu/~ymejova/publications/CompsYelenaMejova.
pdf [2010-02-03].
[23]Melville, P., Gryc, W., & Lawrence, R. D. (2009, June). Sentiment analysis of blogs by combining lexical knowledge with text
classification. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1275-1284).
ACM.
[24]Moe, W. W., & Schweidel, D. A. (2012). Online product opinions:
Incidence, evaluation, and evolution. Marketing Science, 31(3), 372-386.
[25]Mullen, T., & Collier, N. (2004, July). Sentiment Analysis using Support Vector Machines with Diverse Information Sources.
In EMNLP (Vol. 4, pp. 412-418).
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
81
[26]Nigam, K., Lafferty, J., & McCallum, A. (1999, August). Using maximum entropy for text classification. In IJCAI-99 workshop on machine learning for information filtering (Vol. 1, pp. 61-67).
[27]NLProcessor–TextAnalysisToolkit.2000.
http://www.infogistics.com/textanalysis.html
[28]Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?:
sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for
Computational Linguistics.
[29]Pang, B., & Lee, L. (2008). Opinion mining and sentiment
analysis.Foundations and trends in information retrieval, 2(1-2), 1-135.
[30]Rezabakhsh, B., Bornemann, D., Hansen, U., & Schrader, U. (2006).
Consumer power: a comparison of the old economy and the internet economy.Journal of Consumer Policy, 29(1), 3-36.
[31]Rust, R. T., & Oliver, R. W. (1994). Video dial tone: the new world of services marketing. Journal of Services Marketing, 8(3), 5-16.
[32]Snyder, B., & Barzilay, R. (2007). Multiple Aspect Ranking Using the Good Grief Algorithm. In HLT-NAACL (pp. 300-307).
[33]Sridhar, S., & Srinivasan, R. (2012). Social influence effects in online product ratings. Journal of Marketing, 76(5), 70-88.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
82
[34]Thomas, M., Pang, B., & Lee, L. (2006, July). Get out the vote:
Determining support or opposition from Congressional floor-debate transcripts. InProceedings of the 2006 conference on empirical methods in natural language processing (pp. 327-335). Association for Computational Linguistics.
[35]Turney, P. D. (2002, July). 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 (pp. 417-424). Association for Computational Linguistics.
[36]Wang, H., Lu, Y., & Zhai, C. (2010, July). Latent aspect rating analysis on review text data: a rating regression approach.
In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 783-792). ACM.
[37]Whitelaw, C., Garg, N., & Argamon, S. (2005, October). Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp.
625-631). ACM.
[38]Wiebe, J. M., Bruce, R. F., & O'Hara, T. P. (1999, June). Development and use of a gold-standard data set for subjectivity classifications.
In Proceedings of the 37th annual meeting of the Association for
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
83
Computational Linguistics on Computational Linguistics (pp.
246-253). Association for Computational Linguistics.
[39]Yu, H., & Hatzivassiloglou, V. (2003, July). 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 (pp. 129-136).
Association for Computational Linguistics.
[40]Yu, J., Zha, Z. J., Wang, M., & Chua, T. S. (2011, June). Aspect Ranking:
Identifying Important Product Aspects from Online Consumer Reviews.
In ACL(pp. 1496-1505).
[41]Zhang, K., Narayanan, R., & Choudhary, A. (2010, June). Voice of the customers: mining online customer reviews for product feature-based ranking. In3rd Workshop on Online Social Networks.
[42]Zhang, W., Xu, H., & Wan, W. (2012). Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39(11), 10283-10291.