0
Research Papers

Sentiment Root Cause Analysis Based on Fuzzy Formal Concept Analysis and Fuzzy Cognitive Map

[+] Author and Article Information
Sang-Min Park

Department of Computer
Science and Engineering,
Korea University,
145 Anam-ro Seonguk-gu,
Seoul 136-701, Korea
e-mail: wiyard@korea.ac.kr

Young-Gab Kim

Department of Computer and
Information Security,
Sejong University,
209, Neungdong-ro, Gwangjin-gu,
Seoul 143-747, Korea
e-mail: alwaysgabi@sejong.ac.kr

Doo-Kwon Baik

Department of Computer
Science and Engineering,
Korea University,
145 Anam-ro Seonguk-gu,
Seoul 136-701, Korea
e-mail: baikdk@korea.ac.kr

1Corresponding authors.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received December 14, 2015; final manuscript received June 24, 2016; published online August 19, 2016. Assoc. Editor: Jitesh H. Panchal.

J. Comput. Inf. Sci. Eng 16(3), 031004 (Aug 19, 2016) (11 pages) Paper No: JCISE-15-1411; doi: 10.1115/1.4034033 History: Received December 14, 2015; Revised June 24, 2016

Feature-level sentiment analysis can retrieve the sentimental preferences for the features of products but cannot retrieve the causes of the preferences. Previous sentiment analysis methods used sentiment words to calculate the sentiment polarity for specific features but could not utilize neutral sentiment words, even when they constituted a large proportion of the sentiment words. Fault diagnosis can extract causes and determine the root cause by using factual information and the cause-effect relation, but is not used for sentiment data. For the retrieval of sentiment root causes, we propose a sentiment root cause analysis method for user preferences. We consider sentiment relations based on fuzzy formal concept analysis (FFCA) to extend hierarchical feature-level sentiment analysis. A hierarchical relation of neutral sentiment words and explicit causal relation based on causal conjunctions is utilized to retrieve the cross features of root causes. A sentiment root cause is determined from the extracted causes to explain the preference of a sentiment expression by using a fuzzy cognitive map with a relations method. We demonstrate a factual ontology and sentiment ontology based on a feature ontology for clothing products. We evaluated the proposed sentiment root cause analysis method and verified that it is improved as compared with term frequency-based methods and sentiment score analysis.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Scherer, K. R. , 1999, “ Appraisal Theory,” Handbook of Cognition and Emotion, Wiley, Chichester, UK, pp. 637–663.
Tuarob, S. , and Tucker, C. , 2014, “ Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data,” ASME J. Comput. Inf. Sci. Eng., 15(3), p. 031003. [CrossRef]
Rai, R. , 2012, “ Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews,” ASME Paper No. DETC2012-70493.
de Albornoz, J. C. , Plaza, L. , Gervás, P. , and Díaz, A. , 2011, “ A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating,” Advances in Information Retrieval, Springer, Berlin Heidelberg, Germany, pp. 55–66.
Tuarob, S. , and Tucker, C. S. , 2013, “ Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data,” ASME Paper No. DETC2013-12661.
Wei, W. , and Gulla, J. A. , 2010, “ Sentiment Learning on Product Reviews Via Sentiment Ontology Tree,” 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Stroudsburg, PA, pp. 404–413.
Miller, G. A. , 1995, “ WordNet: A Lexical Database for English,” Commun. ACM, 38(11), pp. 39–41. [CrossRef]
Zhou, F. , Jiao, R. , and Linsey, J. S. , 2015, “ Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews,” ASME J. Mech. Des., 137(7), p. 071401. [CrossRef]
Gruber, T. R. , 1993, “ A Translation Approach to Portable Ontology Specifications,” Knowl. Acquis., 5(2), pp. 199–220. [CrossRef]
Maedche, A. , 2002, Ontology Learning for the Sentiment Web, Springer Science & Business Media, Berlin, Germany, pp. 72–79.
Poelmans, J. , Ignatov, D. I. , Kuznetsov, S. O. , and Dedeno, G. , 2013, “ Formal Concept Analysis in Knowledge Processing: A Survey on Applications,” Expert Syst. Appl., 40(16), pp. 6538–6560. [CrossRef]
Cimiano, P. , Hotho, A. , Stumme, G. , and Tane, J. , 2004, “ Conceptual Knowledge Processing With Formal Concept Analysis and Ontologies,” Concept Lattices, Springer, Berlin Heidelberg, Germany, pp. 189–207.
Jiang, G. , Ogasawara, K. , Endoh, A. , and Sakurai, T. , 2003, “ Context-Based Ontology Building Support in Clinical Domains Using Formal Concept Analysis,” Int. J. Med. Inf., 71(1), pp. 71–81. [CrossRef]
Chen, R.-C. , Bau, C.-T. , and Yeh, C.-J. , 2011, “ Merging Domain Ontologies Based on the WordNet System and Fuzzy Formal Concept Analysis techniques,” Appl. Soft Comput., 11(2), pp. 1908–1923. [CrossRef]
Pedersen, T. , Patwardhan, S. , and Michelizzi, J. , 2004, “ WordNet: Similarity: Measuring the Relatedness of Concepts,” HLT-NAACL 2004, Association for Computational Linguistics, Stroudsburg, PA, pp. 38–41.
Stumme, G. , and Maedche, A. , 2001, “ Formal Concept Lattice-Merge: Bottom-Up Merging of Ontologies,” IJCAI, 1, pp. 225–230.
Holzinger, W. , Krüpl, B. , and Herzog, M. , 2006, Using Ontologies for Extracting Product Features From Web Pages, Springer, Berlin Heidelberg, Germany.
Lim, S. C. J. , Liu, Y. , and Loh, H. T. , 2012, “ An Exploratory Study of Ontology-Based Platform Analysis Under User Preference Uncertainty,” ASME Paper No. DETC2012-70756.
Liu, B. , and Zhang, L. , 2012, “ A Survey of Opinion Mining and Sentiment Analysis,” Mining Text Data, Springer, New York, pp. 415–463.
Zhou, F. , Jiao, J. R. , Schaefer, D. , and Chen, S. , 2010, “ Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design,” ASME J. Comput. Inf. Sci. Eng., 10(3), p. 031010. [CrossRef]
Geng, L. , and Hamilton, H. J. , 2006, “ Interestingness Measures for Data Mining: A Survey,” ACM Comput. Surv. (CSUR), 38(3), p. 9. [CrossRef]
Kim, S. , Bak, J. , and Oh, A. , 2011, “ Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations,” Sixth International AAAI Conference on Weblogs and Social Media, pp. 117–126.
Xu, Z. , Zhang, Y. , Wu, Y. , and Yang, Q. , 2012, “ Modeling User Posting Behavior on Social Media,” 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, pp. 545–554.
Vo, A.-D. , and Ock, C.-Y. , 2012, “ Sentiment Classification: a Combination of PMI, sentiWordNet and Fuzzy Function,” Computational Collective Intelligence, Technologies and Applications, Springer, Berlin Heidelberg, Germany, pp. 373–382.
Liu, B. , 2010, “ Sentiment Analysis and Subjectivity,” Handbook of Natural Language Processing 2, Chapman and Hall/CRC, Boca Raton, FL, pp. 627–666.
Baccianella, S. , Esuli, A. , and Sebastiani, F. , 2010, “ SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining,” LREC, 10, pp. 2200–2204.
Hung, C. , and Lin, H.-K. , 2013, “ Using Objective Words in SentiWordNet to Improve Word-of-Mouth Sentiment Classification,” IEEE Intell. Syst., 28(2), pp. 47–54. [CrossRef]
Yi, R. , and Papalambros, P. Y. , 2012, “ On Design Preference Elicitation With Crowd Implicit Feedback,” ASME Paper No. DETC2012-70605.
Stone, T. , and Choi, S.-K. , 2013, “ Extracting Consumer Preference From User-Generated Content Sources Using Classification,” ASME Paper No. DETC2013-13228.
Afshari, H. , Peng, Q. , and Gu, P. , 2016, “ Design Optimization for Sustainable Products Under Users' Preference Changes,” ASME J. Comput. Inf. Sci. Eng., in press.
Wang, L. , Youn, B. D. , Azarm, S. , and Kannan, P. K. , 2011, “ Customer-Driven Product Design Selection Using Web Based User-Generated Content,” ASME Paper No. DETC2011-48338.
Tucker, C. S. , and Kim, H. M. , 2011, “ Trend Mining for Predictive Product Design,” ASME J. Mech. Des., 133(11), p. 111008. [CrossRef]
Marvasti, M. A. , Poghosyan, A. V. , Harutyunyan, S. N. , and Grigoryan, N. M. , 2013, “ An Anomaly Event Correlation Engine: Identifying Root Causes, Bottlenecks, and Black Swans in IT Environments,” VMware Tech. J., 2(1), pp. 35–45.
Jabrouni, H. , Kamsu-Foguem, B. , Geneste, L. , and Vaysse, C. , 2011, “ Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback,” Eng. Appl. Artif. Intell., 24(8), pp. 1419–1431. [CrossRef]
Zhong, B. , and Li, Y. , 2014, “ An Ontological and Semantic Approach for the Construction Risk Inferring and Application,” J. Intell. Rob. Syst., 79(3–4), pp. 449–463.
Lee, H. , and Kwon, S. J. , 2014, “ Ontological Semantic Inference Based on Cognitive Map,” Expert Syst. Appl, 41(6), pp. 2981–2988. [CrossRef]
Kosko, B. , 1986, “ Fuzzy Cognitive Maps,” Int. J. Man-Mach. Stud., 24(1), pp. 65–75. [CrossRef]
Zhou, W. , Liu, Z.-T. , and Zhao, Y. , 2007, “ Ontology Learning by Clustering Based on Fuzzy Formal Concept Analysis,” 31st Annual International IEEE Computer Software and Applications Conference, COMPSAC 2007, Bejing, China, July 24–27, Vol. 1, pp. 204–210.
Tho, Q. T. , Hui, S. C. , Fong, A. C. M. , and Cao, T. H. , 2006, “ Automatic Fuzzy Ontology Generation for Semantic Web,” IEEE Trans. Knowl. Data Eng., 18(6), pp. 842–856. [CrossRef]
Lau, R. Y. , Song, D. , Li, Y. , Cheung, T. C. , and Hao, J. X. , 2009, “ Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning,” IEEE Trans. Knowl. Data Eng., 21(6), pp. 800–813. [CrossRef]
De Maio, C. , Fenza, G. , Loia, V. , and Senatore, S. , 2012, “ Hierarchical Web Resources Retrieval by Exploiting Fuzzy Formal Concept Analysis,” Inf. Process. Manage., 48(3), pp. 399–418. [CrossRef]
Cimiano, P. , Hotho, A. , and Staab, S. , 2005, “ Learning Concept Hierarchies From Text Corpora Using Formal Concept Analysis,” J. Artif. Intell. Res. (JAIR), 24(1), pp. 305–339.
Wei, X. , Luo, X. , Li, Q. , Zhang, J. , and Xu, Z. , 2015, “ Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map,” IEEE Trans. Fuzzy Syst., 23(1), pp. 72–84. [CrossRef]
Du, Y. , Hai, Y. , Xie, C. , and Wang, X. , 2014, “ An Approach for Selecting Seed URLs of Focused Crawler Based on User-Interest Ontology,” Appl. Soft Comput., 14, pp. 663–676. [CrossRef]
Xu, Q. , Zhou, F. , and Jiao, J. R. , 2011, “ Affective-Cognitive Modeling for User Experience With Modular Colored Fuzzy Petri Nets,” ASME J. Comput. Inf. Sci. Eng., 11(1), p. 011004. [CrossRef]
Meng, L. , Huang, R. , and Gu, J. , 2013, “ A Review of Semantic Similarity Measures in Wordnet,” Int. J. Hybrid Inf. Technol., 6(1), pp. 1–12.
Wong, W. , Liu, W. , and Bennamoun, M. , 2012, “ Ontology Learning From Text: A Look Back and Into the Future,” ACM Comput. Surv. (CSUR), 44(4), p. 20. [CrossRef]
Kleinberg, J. M. , Kumar, R. , Raghavan, P. , Rajagopalan, S. , and Tomkins, A. S. , 1999, “ The Web as a Graph: Measurements, Models, and Methods,” Computing and Combinatorics, Springer, Berlin Heidelberg, Germany, pp. 1–17.

Figures

Grahic Jump Location
Fig. 1

Fuzzy formal concept analysis

Grahic Jump Location
Fig. 2

Process of sentiment root cause analysis

Grahic Jump Location
Fig. 3

Merged sentiment ontology and factual ontology based on the feature ontology

Grahic Jump Location
Fig. 4

Merged sentiment ontology and factual ontology

Grahic Jump Location
Fig. 5

Hierarchical triple relation score

Grahic Jump Location
Fig. 6

Fuzzy cognitive map with relations

Grahic Jump Location
Fig. 7

Feature ontology tree based on WordNet similarity

Grahic Jump Location
Fig. 8

Factual ontology tree with feature ontology, product description, and neutral sentiment words

Grahic Jump Location
Fig. 9

Merged sentiment ontology based on the FFCA method and factual ontology based on the feature ontology

Grahic Jump Location
Fig. 10

Ratio of triple for information words, expressive words, and auxiliary words

Grahic Jump Location
Fig. 11

Accuracy of sentiment cause analysis compared with term frequency based analysis and feature based analysis

Grahic Jump Location
Fig. 12

Accuracy of sentiment root cause analysis as compared with term frequency-based analysis, feature-based analysis, and feature/sentiment word-based analysis

Grahic Jump Location
Fig. 13

Accuracy of sentiment root cause analysis as compared with sentiment score analysis, sentiment score analysis on informational sentiment words, and feature/sentiment word-based analysis

Grahic Jump Location
Fig. 14

Sentiment cause ontology with triple node score and triple relation score, which includes the hierarchical structure based on FFCA and the nonhierarchical structure method FCM-R

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In