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Parallelized root cause analysis using cause-related aspect formulation technique (CRAFT)

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Aspect-based opinion mining aims to provide results that aid in effective business decision making. Identifying the aspects, their major and minor causes proves to be the major challenge in this domain. This paper presents a cause-related aspect formulation technique (CRAFT) to perform opinion mining. The CRAFT model incorporates an enhanced aspect extraction module, ontology creation based on aspect and aspect categories, aspect and aspect category metadata repository creation and maintenance and a decision tree-based parallelized boosted ensemble. The proposed CRAFT model is implemented in Spark to incorporate parallelism in the architecture. The process of ontology creation and metadata repository creation aids in effective identification of both implicit and explicit aspects. Experiments were conducted using a customer review benchmark dataset incorporating reviews about five varied products. Comparisons were performed with state-of-the-art models CNN+LP, Popscu and TF-RBM. Comparisons indicate improved performances ranging up to 4% in terms of precision, up to 18% in terms of recall and up to 11% on F1 Scores, indicating the effectiveness of the proposed CRAFT model.

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  1. 1.

    Park D, Kim S (2009) The aspects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electron Commer Res Appl 7(4):399–410

  2. 2.

    Zhu F, Zhang X (2010) Impact of online consumer reviews on sales. The moderating role of product and consumer characteristics. J Mark 74(2):133–148

  3. 3.

    Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 168–177

  4. 4.

    Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of first ACM International Conference on Web Search and Data Mining (WSDM-2008), Stanford University, Stanford, California, USA, pp 231–240

  5. 5.

    Pang B, Lillian L, Shivakumar V (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10. Association for Computational Linguistics

  6. 6.

    Popescu AM, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of EMNLP-2005, pp 3–28

  7. 7.

    Li S, Zhou L, Li Y (2015) Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Inf Process Manag 51(1):58–67

  8. 8.

    Eirinaki M, Pisal S, Singh J (2012) Feature-based opinion mining and ranking. J Comput Syst Sci 78(4):1175–1184

  9. 9.

    Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764–7775

  10. 10.

    Bagheri A, Saraee M, De Jong F (2013) An unsupervised aspect detection model for sentiment analysis of reviews. In: Métais E, Meziane F, Saraee M, Sugumaran V, Vadera S (eds) Natural language processing and information systems. Springer, Berlin, pp 140–151

  11. 11.

    Li Y, Qin Z, Xu W, Guo J (2015) A holistic model of mining product aspects and associated sentiments from online reviews. Multimed Tools Appl 74(23):10177–10194

  12. 12.

    Hai Z, Chang K, Cong G (2012) One seed to find them all: mining opinion features via association. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp 255–264

  13. 13.

    Ma B, Zhang D, Yan Z, Kim T (2013) An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. J Electron Commer Res 14(4):304–314

  14. 14.

    Liu K, Xu L, Zhao J (2015) Co-extracting opinion targets and opinion words from online reviews based on the word alignment model. IEEE Trans Knowl Data Eng 27(3):636–650

  15. 15.

    Yan Z, Xing M, Zhang D, Ma B (2015) EXPRS: an extended pagerank method for product feature extraction from online consumer reviews. Inf Manag 52(7):850–858

  16. 16.

    Samha AK, Li Y, Zhang J (2014) Aspect-based opinion extraction from customer reviews. ArXiv preprint arXiv.1404.1982

  17. 17.

    Cruz FL, Troyano JA, Enríquez F, Ortega FJ, Vallejo CG (2013) Long autonomy or long delay. The importance of domain in opinion mining. Expert Syst Appl 40(8):3174–3184

  18. 18.

    Chen L, Qi L, Wang F (2012) Comparison of feature-level learning methods for mining online consumer reviews. Expert Syst Appl 39(10):9588–9601

  19. 19.

    Huang S, Liu X, Peng X, Niu Z (2012) Fine-grained product features extraction and categorization in reviews opinion mining. In: 12th International Conference on Data Mining Workshops (ICDMW), vol 6, pp 680–686

  20. 20.

    Yang B, Cardie C (2013) Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp 1640–1649

  21. 21.

    Li S, Wang R, Zhou G (2012) Opinion target extraction using a shallow semantic parsing framework. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp 1671–1677

  22. 22.

    Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49

  23. 23.

    Rana TA, Cheah YN (2017) A two-fold rule-based model for aspect extraction. Expert Syst Appl 89:273–285

  24. 24.

    Marrese-Taylor E, Velisquez JD, Bravo-Marquez F, Matsuo Y (2013) Identifying customer preferences about tourism products using an aspect-based opinion mining approach. Procedia Comput Sci 22:182–191

  25. 25.

    Liu Q, Gao Z, Liu B, Zhang Y (2016) Automated rule selection for opinion target extraction. Knowl Based Syst 104:74–88

  26. 26.

    Kang Y, Zhou L (2016) RubE: rule-based methods for extracting product features from online consumer reviews. Inf Manag 54(2):166–176

  27. 27.

    Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21–45. https://doi.org/10.1109/MCAS.2006.1688199

  28. 28.

    Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169–198. https://doi.org/10.1613/jair.614

  29. 29.

    Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1–2):1–39. https://doi.org/10.1007/s10462-009-9124-7

  30. 30.

    Kearns K (1988) Thoughts on hypothesis boosting. Machine learning class project (Unpublished manuscript)

  31. 31.

    Zhou Z-H (2012) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, Boca Raton. ISBN 9780429151095

  32. 32.

    Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27(3):221

  33. 33.

    Rana TA, Cheah Y-N (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459–483

  34. 34.

    Munir K, Anjum MS (2018) The use of ontologies for effective knowledge modelling and information retrieval. Appl Comput Inform 14(2):116–126

  35. 35.

    Sun Q, Niu J, Yao Z, Yan H (2019) Exploring eWOM in online customer reviews: sentiment analysis at a fine-grained level. Eng Appl Artif Intell 81:68–78

  36. 36.

    Lau RY, Li C, Liao SS (2014) Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis. Decis Support Syst 65:80–94

  37. 37.

    Amplayo RK, Song M (2017) An adaptable fine-grained sentiment analysis for summarization of multiple short online reviews. Data Knowl Eng 110:54–67

  38. 38.

    Konys A (2018) Towards knowledge handling in ontology-based information extraction systems. Procedia Comput Sci 126:2208–2218

  39. 39.

    Mohan MJ, Sunitha C, Ganesh A, Jaya A (2016) A study on ontology based abstractive summarization. Procedia Comput Sci 87:32–37

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Correspondence to Blessy Selvam.

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Selvam, B., Ravimaran, S. & Selvam, S. Parallelized root cause analysis using cause-related aspect formulation technique (CRAFT). J Supercomput 75, 5914–5929 (2019). https://doi.org/10.1007/s11227-019-02893-8

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  • Opinion mining
  • Aspect extraction
  • Sentiment mining
  • Boosting
  • Ensemble modeling
  • Decision trees