Mining social network users opinions' to aid buyers' shopping decisions

Bridging the gap between buyers and sellers on the online marketplace.Emulation of the Vygotsky's theory of zone of proximal development (ZPD).Mining social network users opinions' to aid buyers' shopping decisions. More and more online buyers turn to online reviews, while shopping, to get support in their choices. For instance, D'Avanzo and Kuflik (2013) show that more than 80% of buyers, while shopping online, expect user's or professional reviews services, implemented on the seller's website, that can be consulted before their purchase could take place. However, the diffusion of information, that buyers deal with during their shopping experience, makes room to the information and cognitive overload an out-and-out curse. All that is causing sellers adding Web decision support services to help buyers with their decision-making processes and there is a growing number of studies focusing on the enhancing of buyers online shopping decisions with the aim to improve their subjective attitudes towards shopping decisions. More and more sellers add on their side web decision support services that implement decision strategies employed by individuals to arrive at decisions and purchases. This paper introduces a cognitively based procedure (Gopnik et al., 2004) that mines users opinions from specific kinds of market, visually summarizing them in order to alleviate buyers overload and speeding up her/his shopping activity. The proposed approach emulates Vygotsky's theory of zone of proximal development that is well-known in the collaborative learning community (Chiu, 2000).

[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 .