| 參考文獻 |
References
Alahmadi, D. H., & Zeng, X.J. (2015). ISTS: Implicit social trust and sentiment based approach to recommender systems. Expert Systems with Applications, 42(22), 8840–8849.
Balahur, A., Hermida, J. M., & Montoyo, A. (2012). Detecting implicit expressions of emotion in text: A comparative analysis. Decision Support Systems, 53(4), 742–753.
Balahur, A., & Steinberger, R. (2009). Rethinking opinion mining in newspaper articles: From theory to practice and back. In Proceedings of the first workshop on opinion mining and sentiment analysis (pp. 1–12). Seville, Spain: University of Sevilla.
Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., et al. (2009). User interests in social media sites: An exploration with micro-blogs. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1823–1826). Hong Kong: ACM.
Bao, S., Nitta, T., Shindou, D., Yanagisawa, M., & Togawa, N. (2015). A landmark-based route recommendation method for pedestrian walking strategies. In IEEE 4th global conference on consumer electronics (pp. 672–673). Osaka, Japan: IEEE.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986.
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based systems, 46, 109-132.
Bolte, A., Goschke, T., & Kuhl, J. (2003). Emotion and intuition: Effects of positive and negative mood on implicit judgments of semantic coherence. Psychological Science, 14(5), 416–421.
Bruce, R., & Wiebe, J. (1994). Word-sense disambiguation using decomposable models. In Proceedings of the 32nd annual meeting on association for computational linguistics (pp.139–146). Las Cruces: Association for Computational Linguistics.
Chandra, Y., Jiang, L. C., & Wang, C. J. (2016). Mining social entrepreneurship strategies using topic modeling. PLoS One, 11(3), e0151342.
Chen, Y. L., Cheng, L. C., & Chuang, C. N. (2008). A group recommendation system with consideration of interactions among group members. Expert systems with applications, 34(3), 2082-2090.
Chieh-Jen Wang, Shuk-Man Cheng, Lung-Hao Lee, Hsin-Hsi Chen,Wen-shen Liu, Pei-Wen Huang and Shih-Peng Lin (2012). NTUSocialRec: An Evaluation Dataset Constructed from Microblogs for Recommendation Applications in Social Networks. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC), pp. 2328-2333.
Cho, S. L., Chen, H. C., & Cheng, C. M. (2013). Taiwan corpora of Chinese sentiments and relevant psychophysiological data-a study on the norm of Chinese sentimental words. Chinese Journal of Psychology, 55(4), 493–523.
Christensen, I. A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127-14135.
Colombetti, G. (2005). Appraising valence. Journal of Consciousness Studies, 12(8-10), 103–126.
Esparza, S. G., O’Mahony, M. P., & Smyth, B. (2012). Mining the real-time web: A novel approach to product recommendation. Knowledge-Based Systems, 29, 3–11.
Fariss, C. J., Linder, F. J., Jones, Z. M., Crabtree, C. D., Biek, M. A., Ross, A. S. M., et al. (2015). Human rights texts: Converting human rights primary source documents into data. PLoS One, 10(9), e0138935.
Fontaine, J. R., Scherer, K. R., Roesch, E. B., & Ellsworth, P. C. (2007). The world of sentiments is not two-dimensional. Psychological Science, 18(12), 1050–1057.
Galati, D., Sini, B., Tinti, C., & Testa, S. (2008). The lexicon of emotion in the neo-Latin languages. Social Science Information, 47(2), 205–220.
Gavalas, D., Kasapakis, V., Konstantopoulos, C., Pantziou, G., Vathis, N., & Zaroliagis, C. (2015). The eCOMPASS multimodal tourist tour planner. Expert Systems with Applications, 42(21), 7303–7316.
Gavalas, D., Kenteris, M., Konstantopoulos, C., & Pantziou, G. (2012). Web application for recommending personalised mobile tourist routes. IET Software, 6(4), 313–322.
Gillin, P. (2008). Secrets of Social Media Marketing: How to Use Online Conversations and Customer Communities to Turbo-charge Your Business! : Linden Publishing.
Grimmer, J. (2010). A bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases. Political Analysis, 18(1), 1–35.
Guo, G., Zhang, J., & Yorke-Smith, N. (2016). A novel recommendation model regularized with user trust and item ratings. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1607–1620.
Guo, J., Zhang, P., & Guo, L. (2012). Mining hot topics from Twitter streams. Procedia Computer Science, 9, 2008-2011.
Guy, I., Zwerdling, N., Ronen, I., Carmel, D., & Uziel, E. (2010). Social media recommendation based on people and tags. In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval (pp. 194–201). Haifa: ACM.
Hatzivassiloglou, V., & McKeown, K. R. (1997). Predicting the semantic orientation of adjectives. In Proceedings of the eighth conference on European chapter of the association for computational linguistics (pp. 174–181). Spain: Association for Computational Linguistics.
Hidasi, B., & Tikk, D. (2016). General factorization framework for context-aware recommendations. Data Mining and Knowledge Discovery, 30(2), 342–371.
Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 256-276.
Hogenboom, A., van Iterson, P., Heerschop, B., Frasincar, F., & Kaymak, U. (2011). Determining negation scope and strength in sentiment analysis. In IEEE international conference on systems, man, and cybernetics (pp. 2589–2594). The Netherlands: IEEE.
Huffaker, D. (2010). Dimensions of leadership and social influence in online communities. Human Communication Research, 36(4), 593–617.
Jang, H. J., Sim, J., Lee, Y., & Kwon, O. (2013). Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with Applications, 40(18), 7492–7503.
Johnson-Laird, P. N., & Oatley, K. (1989). The language of emotions: An analysis of a semantic field. Cognition & Emotion, 3(2), 81–123.
Joyce, E., & Kraut, R. E. (2006). Predicting continued participation in newsgroups. Journal of Computer-Mediated Communication, 11(3), 723–747.
Kevin, M., Tom, L., Kevin, C., & Aiden, M. (2016). Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system. Journal of Hospitality and Tourism Technology, 7(3), 281–299.
Kiss, C., & Bichler, M. (2008). Identification of influencers—measuring influence in customer networks. Decision Support Systems, 46(1), 233-253.
Koole, S. L., & Rothermund, K. (2011). I feel better but I don′t know why: The psychology of implicit emotion regulation. Cognition & Emotion, 25(3), 389–399.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
Liu, Y. C., & Lin, C. W. (2012). A new method to compose long unknown Chinese keywords. Journal of Information Science, 38(4), 366–382.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444.
Majid, A., Chen, L., Chen, G., Mirza, H. T., Hussain, I., & Woodward, J. (2013). A context-aware personalized travel recommendation system based on geotagged social media data mining. International Journal of Geographical Information Science, 27(4), 662–684.
Mano, H. (1991). The structure and intensity of sentimental experiences: Method and context convergence. Multivariate Behavioral Research, 26(3), 389–411.
Neri, F., Aliprandi, C., Capeci, F., Cuadros, M., & By, T. (2012). Sentiment analysis on social media. In Proceedings of the 2012 international conference on advances in social networks analysis and mining (pp. 951–958). Washington: IEEE Computer Society.
Nguyen, T., Phung, D., Adams, B., & Venkatesh, S. (2013). Event extraction using behaviors of sentiment signals and burst structure in social media. Knowledge and Information Systems, 37(2), 279–304.
Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232–247.
Ong, S. Y. (2016). The impact of user sentiment aroused by the-day-of-the-week on the recommendation effectiveness in microblog. TaoYu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016)an City, Taiwan: National Central University.
Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions. Australia: Cambridge University Press.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on empirical methods in natural language processing (pp. 79–86). Stroudsburg, PA, USA: Association for Computational Linguistics.
Passant, A. (2010, November). dbrec—music recommendations using DBpedia. In International Semantic Web Conference (pp. 209-224). Springer, Berlin, Heidelberg.
Passant, A., Hastrup, T., Bojars, U., & Breslin, J. (2008). Microblogging: A semantic web and distributed approach.
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language. Use: Our words, our selves. Annual Review of Psychology, 54(1), 547–577.
Phelan, O., McCarthy, K., & Smyth, B. (2009). Using twitter to recommend real-time topical news. In Proceedings of the third ACM conference on recommender systems (pp. 385–388). New York: ACM.
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209–228.
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., and Riedl,J. (2002). Getting to know you ： learning new user preferences in recommender systems, Proceedings of the IUT 02, San Francisco, CA, pp.127- 134
Russell, J. A. (1983). Pancultural aspects of the human conceptual organization of sentiments. Journal of Personality and Social Psychology, 45(6), 1281–1288.
Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273–294.
Santu, S. K. K., Sondhi, P., & Zhai, C. (2016). Generative feature language models for mining implicit features from customer reviews. In Proceedings of the 25th ACM international on conference on information and knowledge management (pp. 929–938). New York: ACM.
Scherer, K. R. (1999). Appraisal theory. New York: John Wiley & Sons.
Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458–464.
Singh, R., & Kaur, R. (2015). Sentiment analysis on social media and online review. International Journal of Computer Applications, 121(20), 44–48.
Single Grain. (2017). https://www.singlegrain.com/social-media-news/facebook-news-feed-algorothm/
Accessed 30 August 2017.
SlideShare.(2017). https://www.slideshare.net/bexdeep/plurk-analysis-4136802/
Accessed 30 August 2017
Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in sentiment. Journal of Personality and Social Psychology, 48(4), 813–838.
Smith, S. M., & Petty, R. E. (1996). Message framing and persuasion: A message processing analysis. Personality and Social Psychology Bulletin, 22(3), 257–268.
Song, H., Chu, J., Hu, Y., & Liu, X. (2013). Semantic analysis and implicit target extraction of comments from e-commerce websites. In 2013 fourth world congress on software engineering (pp. 331–335). Piscataway, NJ: IEEE.
Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.
Talmy, L. (2001). Toward a cognitive semantics. Vol. 1: Concept-structuring systems. Vol. 2: Typology and process in concept structuring. Cambridge: MIT Press.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406–418.
Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163–173.
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558.
Turney, P. D. (2002). 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). Philadelphia: Association for Computational Linguistics.
Valcarce, D., Parapar, J., & Barreiro, Á. (2016). Item-based relevance modelling of recommendations for getting rid of long tail products. Knowledge-Based Systems, 103, 41–51.
Wang, K. Y., Ting, I. H., & Wu, S., Liu, Q., (2013). Discovering interest groups for marketing in virtual communities: An integrated approach. Journal of Business Research, 66(9), 1360–1366.
Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological Bulletin, 98(2), 219–235.
Wei, T. H. (2016). The effect of event sentiment on product recommendation in a microblog platform. TaoYu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016)an City, Taiwan: National Central University.
Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing. Stroudsburg, PA: Association for Computational Linguistics.
Wu, S., Liu, Q., Wang, L., & Tan, T. , S., Liu, Q., Wang, L., & Tan, T. (2016). Contextual operation for recommender systems. IEEE Transactions on Knowledge and Data Engineering, 28(8), 2000–2012.
Xu, K. (2011). Mining and analyzing customer opinions/sentiments of Web 2.0 for business applications. Hong Kong: City University of Hong Kong.
Xu, Y., Hu, T., & Li, Y. (2016). A travel route recommendation algorithm with personal preference. In 2016 12th international conference on natural computation, fuzzy systems and knowledge discovery (pp. 390–396). Piscataway, NJ: IEEE.
Xue, S., & Liu, S. (2015). Algorithm research of individualized travelling route recommendation based on similarity. In MATEC Web of Conferences. Les Ulis, France: EDP Sciences.
Yang, G. (2011). Technology and its contents: Issues in the study of the Chinese internet. Journal of Asian Studies, 70(4), 1043–1050.
Yessenov, K., & Misailovi, S. Sentiment analysis of movie review comments, 6.863 Spring 2009 final project. Kuat Yessenov kuat@csail.mit.edu. Saša Misailovic.
Yigit, M., Bilgin, B. E., & Karahoca, A. (2015). Extended topology based recommendation system for unidirectional social networks. Expert Systems with Applications, 42(7), 3653–3661.
Yu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016), Featuring, detecting, and visualizing human sentiment in Chinese micro-blog. ACM Transactions on Knowledge Discovery from Data, 10(4), 1–23.
Zhang, Z. (2008). Weighing stars: Aggregating online product reviews for intelligent e-commerce applications. IEEE Intelligent Systems, 23(5), 42–49.
Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56, 211–222.
|
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit