Mining social network users opinions' to aid buyers' shopping decisions
暂无分享,去创建一个
[1] Inés González-González,et al. The Implementation of Process Management: A System to Increase Business Efficiency - Empirical Study of Spanish Companies , 2012, Int. J. Knowl. Soc. Res..
[2] K. Bruffee. Collaborative Learning: Higher Education, Interdependence, and the Authority of Knowledge , 1995 .
[3] Wan-Sup Cho,et al. Voice of Customer Analysis for Internet Shopping Malls , 2013 .
[4] H. Simon,et al. Rational choice and the structure of the environment. , 1956, Psychological review.
[5] Pranjal Gupta,et al. How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective , 2010 .
[6] John G. Lynch,et al. Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces , 1997 .
[7] Won Jun Lee,et al. Psychological reactance to online recommendation services , 2009, Inf. Manag..
[8] Donald R. Jones,et al. How Well Do E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies? An Exploratory Study , 2008, Int. J. E Bus. Res..
[9] Hersen Doong,et al. Online customers' cognitive differences and their impact on the success of recommendation agents , 2010, Inf. Manag..
[10] Mukesh A. Zaveri,et al. Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges , 2014, Appl. Comput. Intell. Soft Comput..
[11] Athanasios Drigas,et al. Business to Consumer (B2C) E-Commerce Decade Evolution , 2013, Int. J. Knowl. Soc. Res..
[12] Miltiadis D. Lytras,et al. Software Technologies in Knowledge Society , 2011, J. Univers. Comput. Sci..
[13] Lael J. Schooler,et al. Five Principles for Studying People's Use of Heuristics , 2010 .
[14] Allen Newell,et al. Computer science as empirical inquiry: symbols and search , 1976, CACM.
[15] Efraim Turban,et al. Decision Support and Business Intelligence Systems (8th Edition) , 2006 .
[16] Dongmei Zhang,et al. A comparison study of multi-class sentiment classification for Chinese reviews , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.
[17] Tsvi Kuflik,et al. E-Commerce Websites Services versus Buyers Expectations: an Empirical Analysis of the Online Marketplace , 2013, Int. J. Inf. Technol. Decis. Mak..
[18] G. Pólya,et al. How to Solve It. A New Aspect of Mathematical Method. , 1945 .
[19] José María Moreno-Jiménez,et al. A Quantitative Approach to Identify the Arguments that Support Decisions in E-Cognocracy , 2011, Int. J. Knowl. Soc. Res..
[20] Maria Sicilia,et al. The effects of the amount of information on cognitive responses in online purchasing tasks , 2010, Electron. Commer. Res. Appl..
[21] Chin-Sheng Yang,et al. A Rule-Based Approach For Effective Sentiment Analysis , 2012, PACIS.
[22] Herbert A. Simon,et al. Computer Science as Empirical Inquiry , 2011 .
[23] Paul Resnick,et al. Recommender systems , 1997, CACM.
[24] Giovanni Pilato,et al. An Approach to Detect Polarity Variation Rules for Sentiment Analysis , 2014, WEBIST.
[25] Rudy Prabowo,et al. Sentiment analysis: A combined approach , 2009, J. Informetrics.
[26] Giovanni Pilato,et al. A Study on Classification Methods Applied to Sentiment Analysis , 2013, 2013 IEEE Seventh International Conference on Semantic Computing.
[27] G. Häubl,et al. Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents , 2003 .
[28] Byung-Kwan Lee,et al. The effect of information overload on consumer choice quality in an on-line environment , 2004 .
[29] Manas Ranjan Patra,et al. Web-services classification using intelligent techniques , 2010, Expert Syst. Appl..
[30] D. Read. Judgment and Choice , 2005 .
[31] David M. Sobel,et al. A theory of causal learning in children: causal maps and Bayes nets. , 2004, Psychological review.
[32] Izak Benbasat,et al. E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..
[33] Gerald Häubl,et al. Personalization without Interrogation: Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents , 2009 .
[34] Tsvi Kuflik,et al. Building and using domain ontologies for learning in various domains: a semantic web-based learning perspective , 2008, Int. J. Knowl. Learn..
[35] J. E. Russo,et al. More Information Is Better: A Reevaluation of Jacoby, Speller and Kohn , 1974 .
[36] Ellen Riloff,et al. Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.
[37] Purushottam Papatla,et al. Google or BizRate? How search engines and comparison sites affect unplanned choices of online retailers , 2009 .
[38] R. M. Chandrasekaran,et al. Measuring the quality of hybrid opinion mining model for e-commerce application , 2014 .
[39] M. Burd,et al. Can irrational behaviour maximise fitness? , 2008, Behavioral Ecology and Sociobiology.
[40] Efraim Turban,et al. Decision Support and Business Intelligence Systems (8th Edition) , 2006 .
[41] Pankoo Kim,et al. Analysis on Smartphone Related Twitter Reviews by Using Opinion Mining Techniques , 2014 .
[42] B. Schwartz,et al. Doing Better but Feeling Worse , 2006, Psychological science.
[43] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[44] Suad Alhojely,et al. Sentiment Analysis and Opinion Mining: A Survey , 2016 .
[45] Wen Shi,et al. Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).
[46] Jesfis Peral,et al. Heuristics -- intelligent search strategies for computer problem solving , 1984 .
[47] David M. Pennock,et al. Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.
[48] Allen Newell,et al. Computer science as empirical inquiry: symbols and search (1976) , 1989 .
[49] José María Moreno-Jiménez,et al. A new e-learning tool for cognitive democracies in the Knowledge Society , 2014, Comput. Hum. Behav..
[50] Margaret H. Szymanski,et al. LEARNING IN DOING: SOCIAL, COGNITIVE AND COMPUTATIONAL PERSPECTIVES , 2011 .
[51] Pamela J. Wisniewski,et al. When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity , 2010, Comput. Hum. Behav..
[52] Daniel Dajun Zeng,et al. Information Overload and Viral Marketing: Countermeasures and Strategies , 2010, SBP.
[53] Ching-Torng Lin,et al. Application of salesman-like recommendation system in 3G mobile phone online shopping decision support , 2010, Expert Syst. Appl..
[54] Ming Ming Chiu,et al. Group Problem-Solving Processes: Social Interactions and Individual Actions , 2000 .
[55] G. Gigerenzer. Gut Feelings: The Intelligence of the Unconscious , 2007 .
[56] Wolfgang Maass,et al. In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences , 2010, Comput. Hum. Behav..
[57] Izak Benbasat,et al. A study of demographic embodiments of product recommendation agents in electronic commerce , 2010, Int. J. Hum. Comput. Stud..
[58] Yuan Wen Hau,et al. Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip , 2014, Appl. Comput. Intell. Soft Comput..
[59] Leonard Adelman,et al. How Web Site Decision Technology Affects Consumers , 2002, IEEE Internet Comput..
[60] Bing Liu,et al. Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.
[61] Pierre Sens,et al. Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective , 2015, ANT/SEIT.
[62] Peter D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.
[63] Pentti Kanerva,et al. Sparse Distributed Memory , 1988 .
[64] Yu Zhao,et al. Analysis of the user behavior and opinion classification based on the BBS , 2008, Appl. Math. Comput..
[65] Monika Kukar-Kinney,et al. The determinants of consumers’ online shopping cart abandonment , 2010 .
[66] Antti Oulasvirta,et al. When more is less: the paradox of choice in search engine use , 2009, SIGIR.
[67] Lina Zhou,et al. Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.
[68] Miltiadis D. Lytras,et al. Improving e-learning communities through optimal composition of multidisciplinary learning groups , 2014, Comput. Hum. Behav..
[69] Inés González-González,et al. The Implementation of Process Management: A System to Increase Business Efficiency—Empirical Study of Spanish Companies , 2012 .
[70] Jorge E. Araña,et al. Understanding the use of non-compensatory decision rules in discrete choice experiments: The role of emotions , 2009 .
[71] Michael L. Littman,et al. Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.
[72] Valerie J. Trifts,et al. Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .