Understanding and Predicting Users’ Rating Behavior: A Cognitive Perspective
Published Online:24 Apr 2020https://doi.org/10.1287/ijoc.2019.0919
References
- (2007) Understanding mindshift learning: The transition to object-oriented development. Management Inform. Systems Quart. 31(3):453–474.Crossref, Google Scholar
- (2017) Associated activation-driven enrichment: Understanding implicit information from a cognitive perspective. IEEE Trans. Knowledge Data Engrg. 29(12):2655–2668.Crossref, Google Scholar
- (2012) Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Anal. 20(3):351–368.Crossref, Google Scholar
- (2007) Attention issues in spatial information systems: Directing mobile users’ visual attention using augmented reality. J. Management Inform. Systems 23(4):163–184.Crossref, Google Scholar
- (2011) Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspect. Psych. Sci. 6(1):3–5.Crossref, Google Scholar
- (2017) Efficient vector representation for documents through corruption. Proc. 5th Internat. Conf. Learn. Representations, Toulon, France.Google Scholar
- (2016) Neural sentiment classification with user and product attention. Proc. 2016 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Austin, TX), 1650–1659.Google Scholar
- (2017) Capturing user and product information for document level sentiment analysis with deep memory network. Proc. 2017 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Copenhagen), 521–526.Google Scholar
- (2010) Brand positioning strategy using search engine marketing. Management Inform. Systems Quart. 34(2):261–279.Crossref, Google Scholar
- (2012) Multi-criteria ratings for recommender systems: An empirical analysis in the tourism domain. Internat. Conf. Electronic Commerce Web Tech. (Springer, Berlin, Heidelberg), 100–111.Crossref, Google Scholar
- (2012) Classification of customer reviews based on sentiment analysis. 19th Conf. Inform. Comm. Tech. Tourism (ENTER) (Springer, Helsingborg, Sweden), 460–470.Crossref, Google Scholar
- (2010) Capturing the stars: Predicting ratings for service and product reviews. Proc. Human Language Tech.: 11th Annual Conf. North Amer. Chapter Assoc. Comput. Linguistics (NAACL HLT) 2010 Workshop Semantic Search (Association for Computational Linguistics, Los Angeles), 36–43.Google Scholar
- (2014) Do we order product review information display? How? Inform. Management 51(7):883–894.Crossref, Google Scholar
- (2016) Jointly modeling review content and aspect ratings for review rating prediction. Proc. 39th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, Pisa, Italy), 893–896.Google Scholar
- (2014) A convolutional neural network for modelling sentences. Proc. 52nd Annual Meeting Assoc. Comput. Linguistics (Vol. 1: Long Papers) (Association for Computational Linguistics, Baltimore), 655–665.Google Scholar
- (2006) Understanding conceptual schemas: Exploring the role of application and IS domain knowledge. Inform. Systems Res. 17(1):81–99.Link, Google Scholar
- (2014) Convolutional neural networks for sentence classification. Proc. 2014 Conf. Empirical Methods Natural Language Processing (EMNLP) (Association for Computational Linguistics, Doha, Qatar), 1746–1751.Google Scholar
- (2015) A method for stochastic optimization. Internat. Conf. Learn. Representations (ICLR), San Diego, CA.Google Scholar
- (2015) Skip-thought vectors. Proc. 29th Annual Conf. Neural Inform. Processing Systems (NIPS) (Curran Associates, Red Hook, NY), 3294–3302.Google Scholar
- (2009) Toward a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Trans. Knowledge Data Engrg. 21(6):800–813.Crossref, Google Scholar
- (2014) Distributed representations of sentences and documents. Proc. 31st Internat. Conf. Machine Learn., Beijing, China, 1188–1196.Google Scholar
- (2017) Prediction and analysis of hotel ratings from crowd-sourced data. World Conf. Inform. Systems Tech., Porto Santo Island, Madeira, Portugal, 493–502.Google Scholar
- (2015) Deep learning. Nature 521(7553):436–444.Crossref, Google Scholar
- (2018) Document-level multi-aspect sentiment classification by jointly modeling users, aspects, and overall ratings. Proc. 27th Internat. Conf. Comput. Linguistics, Santa Fe, NM, 925–936.Google Scholar
- (2017) Interactive attention networks for aspect-level sentiment classification. Proc. 26th Internat. Joint Conf. Artificial Intelligence, Melbourne, Australia, 4068–4074.Google Scholar
- (2003) Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist 38(1):43–52.Crossref, Google Scholar
- (2013a) Efficient estimation of word representations in vector space. Internat. Conf. Learn. Representations (ICLR) Workshop Track, Scottsdale, AZ.Google Scholar
- (2013b) Distributed representations of words and phrases and their compositionality. Adv. Neural Inform. Processing Systems 26:3111–3119.Google Scholar
- (2011) Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. Proc. 34th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 665–674.Google Scholar
- (2010) What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quart. 34(1):185–200.Google Scholar
- (1970) Information and consumer behavior. J. Political Econom. 78(2):311–329.Crossref, Google Scholar
- (2010) The bag-of-opinions method for review rating prediction from sparse text patterns. Proc. 23rd Internat. Conf. Comput. Linguistics (Tsinghua University Press, Beijing), 913–921.Google Scholar
- (2017) Schemata: The building blocks of cognition. Spiro RJ, Bruce BC, Brewer WF, eds. Theoretical Issues in Reading Comprehension (Routledge, Abingdon, UK), 33–58.Crossref, Google Scholar
- (2015) A neural attention model for abstractive sentence summarization. Proc. 2015 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Lisbon, Portugal), 379–389.Crossref, Google Scholar
- (2007) Multiple aspect ranking using the good grief algorithm. Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics (Association for Computational Linguistics, Rochester, NY), 300–307.Google Scholar
- (1993) Whole-part-whole learning model. Performance Improvement Quart. 6(1):43–53.Crossref, Google Scholar
- (1988) Cognitive load during problem solving: Effects on learning. Cognitive Sci. 12(2):257–285.Crossref, Google Scholar
- (2015) Document modeling with gated recurrent neural network for sentiment classification. Proc. 2015 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Lisbon, Portugal), 1422–1432.Google Scholar
- (2016) Aspect level sentiment classification with deep memory network. Proc. 2016 Conf. Empirical Methods Natl. Language Processing (Association for Computational Linguistics, Austin), 214–224.Google Scholar
- (2015a) The effects of repeating purchase cues and mixed reviews on product attribution. Proc. 36th Internat. Conf. Inform. Systems, Fort Worth, TX.Google Scholar
- (2015b) The effect of conflicting consumer reviews on the accuracy of a purchase decisions. 21st Americas Conf. Inform. Systems, Puerto Rico.Google Scholar
- (2007) Context representation for web search results. J. Inform. Sci. 33(1):77–94.Crossref, Google Scholar
- (2009) Interactive decision aids for consumer decision making in e-commerce: The influence of perceived strategy restrictiveness. Management Inform. Systems Quart. 33(2):293–320.Crossref, Google Scholar
- (2010) Latent aspect rating analysis on review text data: A rating regression approach. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 783–792.Google Scholar
- (2011) Latent aspect rating analysis without aspect keyword supervision. Proc. 17th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 618–626.Google Scholar
- (2016) Attention-based LSTM for aspect-level sentiment classification. Proc. 2016 Conf. Empirical Methods Natl. Language Processing, Austin, TX, 606–615.Google Scholar
- (2013) Cognitive perspectives in psychology. Handbook of Research on Educational Communications and Technology (Routledge, Abingdon, UK), 90–123.Google Scholar
- (2016) Hierarchical attention networks for document classification. Proc. 2016 Conf. North Amer. Chapter Assoc. Comput. Linguistics, San Diego, CA, 1480–1489.Google Scholar
- (2013) Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. Management Inform. Systems Quart. 38(2):539–560.Google Scholar
- (2011) Aspect ranking: Identifying important product aspects from online consumer reviews. Proc. 49th Annual Meeting Assoc. Computational Linguistics: Human Language Technologies, vol. 1 (Association for Computational Linguistics, Stroudsburg, PA), 1496–1505.Google Scholar
- (2014) Product aspect ranking and its applications. IEEE Trans. Knowledge Data Engrg. 26(5):1211–1224.Crossref, Google Scholar

