Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis

In the era of Web 2.0, there has been an explosive growth of consumer-contributed comments at social media and electronic commerce Web sites. Applying state-of-the-art social analytics methodology to analyze the sentiments embedded in these consumer comments facilitates both firms' product design strategies and individual consumers' comparison shopping. However, existing social analytics methods often adopt coarse-grained and context-free sentiment analysis approaches. Consequently, these methods may not be effective enough to support firms and consumers' demands of fine-grained extraction of market intelligence from social media. Guided by the design science research methodology, the main contribution of our research is the design of a novel social analytics methodology that can leverage the sheer volume of consumer reviews archived at social media sites to perform a fine-grained extraction of market intelligence. More specifically, the proposed social analytics methodology is underpinned by a novel semi-supervised fuzzy product ontology mining algorithm. Evaluated based on real-world social media data, our prototype system shows remarkable performance improvement over a baseline ontology learning system and a context-free sentiment analysis system. The managerial implication of our research is that firms can apply the proposed social analytics methodology to tap into the collective social intelligence on the Web, and hence improve their product design and marketing strategies.

[1]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[2]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[3]  Chun Chen,et al.  Opinion Word Expansion and Target Extraction through Double Propagation , 2011, CL.

[4]  Lina Zhou,et al.  Ontology-supported polarity mining , 2008, J. Assoc. Inf. Sci. Technol..

[5]  Diego Reforgiato Recupero,et al.  AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis , 2008, IEEE Intelligent Systems.

[6]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[7]  Andrea Esuli,et al.  Determining the semantic orientation of terms through gloss classification , 2005, CIKM '05.

[8]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[9]  Kam-Fai Wong,et al.  An intelligent information agent for document title classification and filtering in document-intensive domains , 2007, Decis. Support Syst..

[10]  Rada Mihalcea,et al.  Multilingual Sentiment and Subjectivity Analysis , 2011 .

[11]  Victoria Y. Yoon,et al.  Semantic similarity of ontology instances using polarity mining , 2013, J. Assoc. Inf. Sci. Technol..

[12]  Raymond Y. K. Lau,et al.  Towards a belief-revision-based adaptive and context-sensitive information retrieval system , 2008, TOIS.

[13]  CHENGXIANG ZHAI,et al.  A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.

[14]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[15]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[16]  Raymond Y. K. Lau,et al.  Toward a semantic granularity model for domain-specific information retrieval , 2011, TOIS.

[17]  Thomas L. Griffiths,et al.  Learning author-topic models from text corpora , 2010, TOIS.

[18]  Bing Liu,et al.  The utility of linguistic rules in opinion mining , 2007, SIGIR.

[19]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[20]  Hsinchun Chen,et al.  Evaluating sentiment in financial news articles , 2012, Decis. Support Syst..

[21]  Yue Lu,et al.  Latent aspect rating analysis without aspect keyword supervision , 2011, KDD.

[22]  Alex Wright Our sentiments, exactly , 2009, CACM.

[23]  Hsinchun Chen,et al.  Social Media Analytics and Intelligence , 2010, IEEE Intell. Syst..

[24]  Michael L. Littman,et al.  Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.

[25]  Oren Etzioni,et al.  OPINE: Extracting Product Features and Opinions from Reviews , 2005, HLT/EMNLP.

[26]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Claire Cardie,et al.  OpinionFinder: A System for Subjectivity Analysis , 2005, HLT.

[28]  Alice H. Oh,et al.  Aspect and sentiment unification model for online review analysis , 2011, WSDM '11.

[29]  Raymond Y. K. Lau,et al.  Automatic Domain Ontology Extraction for Context-Sensitive Opinion Mining , 2009, ICIS.

[30]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[31]  W. Bruce Croft,et al.  A language modeling approach to information retrieval , 1998, SIGIR '98.

[32]  Amal Zouaq,et al.  Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project , 2009, IEEE Transactions on Knowledge and Data Engineering.

[33]  Daniel Dajun Zeng,et al.  Fine-grained opinion mining by integrating multiple review sources , 2010, J. Assoc. Inf. Sci. Technol..

[34]  Arjun Mukherjee,et al.  Aspect Extraction through Semi-Supervised Modeling , 2012, ACL.

[35]  Panagiotis G. Ipeirotis,et al.  Show me the money!: deriving the pricing power of product features by mining consumer reviews , 2007, KDD '07.

[36]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[37]  Yuefeng Li,et al.  A Personalized Ontology Model for Web Information Gathering , 2011, IEEE Transactions on Knowledge and Data Engineering.

[38]  Andrzej Bargiela,et al.  Probabilistic Topic Models for Learning Terminological Ontologies , 2010, IEEE Transactions on Knowledge and Data Engineering.

[39]  Hsinchun Chen,et al.  Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums , 2008, TOIS.

[40]  Ruwei Dai,et al.  An integration strategy for mining product features and opinions , 2008, CIKM '08.

[41]  Yang Yu,et al.  Semantic mining on customer survey , 2012, I-SEMANTICS '12.

[42]  Steffen Staab,et al.  Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis , 2005, J. Artif. Intell. Res..

[43]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic Web , 2006, IEEE Transactions on Knowledge and Data Engineering.

[44]  Jon Atle Gulla,et al.  Sentiment Learning on Product Reviews via Sentiment Ontology Tree , 2010, ACL.

[45]  Raymond Y. K. Lau,et al.  Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning , 2009, IEEE Transactions on Knowledge and Data Engineering.

[46]  W. Bruce Croft,et al.  Deriving concept hierarchies from text , 1999, SIGIR '99.

[47]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[48]  Jer Lang Hong,et al.  A Novel Ontological Technique for Sentiment Analysis , 2012, ICONIP.

[49]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[50]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[51]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[52]  Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.

[53]  Yuji Matsumoto,et al.  Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining , 2007, EMNLP.

[54]  Hsinchun Chen,et al.  AI and Opinion Mining , 2010, IEEE Intelligent Systems.

[55]  Yu-Liang Chi Elicitation synergy of extracting conceptual tags and hierarchies in textual document , 2007, Expert Syst. Appl..

[56]  Yorick Wilks,et al.  Named Entity Recognition from Diverse Text Types , 2001 .

[57]  Raymond Y. K. Lau,et al.  A two-stage decision model for information filtering , 2012, Decis. Support Syst..

[58]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.