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題名 隱藏意見萃取--辨識多世代商品之關鍵特色
Latent Opinion Extraction: Identify Critical Product Features in Multiple Generations作者 林沛盈 貢獻者 唐揆
林沛盈關鍵詞 意見探勘
情緒分析
面相偵測
社會影響
遺失值填值
Opinion Mining
Sentiment Analysis
Aspect Identification
Social Influence
Missing Data Imputation日期 2012 上傳時間 2-一月-2014 13:43:33 (UTC+8) 摘要 隨著網路的普及，消費者將許多自身經驗撰寫成產品評論放上網站，使得消費者與廠商之間的資訊不對稱得以下降，同時產生了讓廠商無法忽視的口碑效應。根據兩項針對超過兩千名美國成年人的研究，有81%的網路使用者曾為一項商品上網進行至少一次的資料搜尋，其中有73%到87%的人表示網路評論對他們的購買意願產生了顯著的影響，特別是高涉入性產品。在消費者與廠商之間的權力結構逐漸往消費者端傾斜的網路時代，廠商必須擁有在許多評論網站中快速彙整資料的能力。因此，針對評論做意見探勘，是非常重要的研究議題。而在意見探勘的領域中，若能分辨不同面相間的重要性差異，對廠商而言，可藉此判斷那些面相較能左右銷售量與使用者滿意度，本研究著重於探討消費者認為「重要面相」的研究。然而，過去的研究較少討論到評論依據時間，後發表的評論會被前述評論影響的議題。本研究發現依據傳統的面相意見探勘，將會產生面相分數與整體分數不一致性的狀態，顯示消費者應有隱而未現的意見未被充分表達。本研究首度考慮了評論之間的關聯性，並以此發展填值方法。此外，本研究針對Amazon網站上Canon數位相機SX210、SX230，及SX260等三個世代數位相機的消費者評論提出GPA、MPA Matrix之分析架構。分析結果清楚指出該系列相機不同世代間的正向與負向面相。透過本研究的自動分析架構，廠商可以從數千筆消費者評論中，有效率且更精準的找到消費者滿意與需改善之面相。
A growing number of consumers have written product reviews to share their own experience on the Internet. The development decreases information asymmetry between consumers and manufactures and causes e-word of mouth effect that firms could not ignore. According to the survey of more than 2000 adults in the U.S., 81% of Internet users had searched for product information they planned to buy at least one time. Between 73% and 87% Internet users said the product reviews influenced their purchase intention, especially in high involvement products. Consequently, it is essential for manufactures to have the ability to summarize thousands of consumer product reviews into useful information in a short time. Thus, review opinion mining becomes an important issue in the recent years. In the field of review opinion mining, it is critical for manufactures to differentiate product features in terms of their importance. According to the data from aspect opinion mining, manufactures can determine which product feature significantly influences sales volume and customer satisfaction. Therefore, our research focused on identifying “critical product features.” We found existing studies did not address the time-effects on product reviews. That is, consumer review might be influenced by the foregoing reviews. The time-effects will cause inconsistent between the overall score and the feature score while the data based on the traditional aspect opinion mining method. Our research took the inconsistent situation into consideration, and developed an imputation method for features missing in reviews. In addition, we analyzed the sentiment polarity of Canon digital camera (SX210, SX230, SX260 generations) on Amazon with GPA, MPA Matrix. The results clearly identify the positive and negative features in different product generations. Using the automatic sentiment analysis framework we propose, manufactures could find the critical features that receive very favorable responses from consumers or need improvement in an efficient and more accurate way.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 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 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).[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).[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.[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 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. 描述 碩士
國立政治大學
企業管理研究所
100355026
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100355026 資料類型 thesis dc.contributor.advisor 唐揆 zh_TW dc.contributor.author (作者) 林沛盈 zh_TW dc.creator (作者) 林沛盈 zh_TW dc.date (日期) 2012 en_US dc.date.accessioned 2-一月-2014 13:43:33 (UTC+8) - dc.date.available 2-一月-2014 13:43:33 (UTC+8) - dc.date.issued (上傳時間) 2-一月-2014 13:43:33 (UTC+8) - dc.identifier (其他 識別碼) G0100355026 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63189 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 企業管理研究所 zh_TW dc.description (描述) 100355026 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 隨著網路的普及，消費者將許多自身經驗撰寫成產品評論放上網站，使得消費者與廠商之間的資訊不對稱得以下降，同時產生了讓廠商無法忽視的口碑效應。根據兩項針對超過兩千名美國成年人的研究，有81%的網路使用者曾為一項商品上網進行至少一次的資料搜尋，其中有73%到87%的人表示網路評論對他們的購買意願產生了顯著的影響，特別是高涉入性產品。在消費者與廠商之間的權力結構逐漸往消費者端傾斜的網路時代，廠商必須擁有在許多評論網站中快速彙整資料的能力。因此，針對評論做意見探勘，是非常重要的研究議題。而在意見探勘的領域中，若能分辨不同面相間的重要性差異，對廠商而言，可藉此判斷那些面相較能左右銷售量與使用者滿意度，本研究著重於探討消費者認為「重要面相」的研究。然而，過去的研究較少討論到評論依據時間，後發表的評論會被前述評論影響的議題。本研究發現依據傳統的面相意見探勘，將會產生面相分數與整體分數不一致性的狀態，顯示消費者應有隱而未現的意見未被充分表達。本研究首度考慮了評論之間的關聯性，並以此發展填值方法。此外，本研究針對Amazon網站上Canon數位相機SX210、SX230，及SX260等三個世代數位相機的消費者評論提出GPA、MPA Matrix之分析架構。分析結果清楚指出該系列相機不同世代間的正向與負向面相。透過本研究的自動分析架構，廠商可以從數千筆消費者評論中，有效率且更精準的找到消費者滿意與需改善之面相。 zh_TW dc.description.abstract (摘要) A growing number of consumers have written product reviews to share their own experience on the Internet. The development decreases information asymmetry between consumers and manufactures and causes e-word of mouth effect that firms could not ignore. According to the survey of more than 2000 adults in the U.S., 81% of Internet users had searched for product information they planned to buy at least one time. Between 73% and 87% Internet users said the product reviews influenced their purchase intention, especially in high involvement products. Consequently, it is essential for manufactures to have the ability to summarize thousands of consumer product reviews into useful information in a short time. Thus, review opinion mining becomes an important issue in the recent years. In the field of review opinion mining, it is critical for manufactures to differentiate product features in terms of their importance. According to the data from aspect opinion mining, manufactures can determine which product feature significantly influences sales volume and customer satisfaction. Therefore, our research focused on identifying “critical product features.” We found existing studies did not address the time-effects on product reviews. That is, consumer review might be influenced by the foregoing reviews. The time-effects will cause inconsistent between the overall score and the feature score while the data based on the traditional aspect opinion mining method. Our research took the inconsistent situation into consideration, and developed an imputation method for features missing in reviews. In addition, we analyzed the sentiment polarity of Canon digital camera (SX210, SX230, SX260 generations) on Amazon with GPA, MPA Matrix. The results clearly identify the positive and negative features in different product generations. Using the automatic sentiment analysis framework we propose, manufactures could find the critical features that receive very favorable responses from consumers or need improvement in an efficient and more accurate way. en_US dc.description.tableofcontents 摘要 3Abstract 4致謝詞 6第一章 緒論 131.1 研究背景 131.2 研究動機與目的 141.3 研究方法與架構 161.4 研究結果與貢獻 181.5 論文結構 18第二章 文獻回顧 192.1面相萃取 192.2 情緒分析 222.2.1 監督式情緒偵測 232.2.2非監督式情緒偵測 272.3 重要面相彙整 29第三章 研究方法 323.1 資料前處理 333.2 面相辨識與情緒偵測 353.2.1 面相辨識 353.2.2 面相標籤 383.2.3 情緒偵測 393.3 不一致性改善 413.3.1填值偵測 413.3.2填入順序 423.3.3填值方式GM (Group Majority Value) 433.4 重要面相彙整 443.4.1 GPA Matrix 443.4.2 迴歸與決策樹分析 463.4.3 MPA Matrix 47第四章 研究結果 494.1Canon Sx系列三世代產品描述 494.2原始資料描繪 514.3至少考量數目決定 544.3 GPA、迴歸與決策樹分析 604.3.1 GPA Matrix 604.3.2 迴歸分析 664.3.3 決策樹分析 694.4 MPA彙整結果 72第五章 結論 76參考文獻 77 zh_TW dc.format.extent 2317822 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100355026 en_US dc.subject (關鍵詞) 意見探勘 zh_TW dc.subject (關鍵詞) 情緒分析 zh_TW dc.subject (關鍵詞) 面相偵測 zh_TW dc.subject (關鍵詞) 社會影響 zh_TW dc.subject (關鍵詞) 遺失值填值 zh_TW dc.subject (關鍵詞) Opinion Mining en_US dc.subject (關鍵詞) Sentiment Analysis en_US dc.subject (關鍵詞) Aspect Identification en_US dc.subject (關鍵詞) Social Influence en_US dc.subject (關鍵詞) Missing Data Imputation en_US dc.title (題名) 隱藏意見萃取--辨識多世代商品之關鍵特色 zh_TW dc.title (題名) Latent Opinion Extraction: Identify Critical Product Features in Multiple Generations en_US dc.type (資料類型) thesis en dc.relation.reference (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 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 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).[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).[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.[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). 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