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Seerangan, Naveenkumar
- Novel Multi-Level Aspect Based Sentiment Analysis for Improved Root-Cause Analysis
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Authors
Affiliations
1 Department of Computer Science, Bharathiar University, IN
2 Department of Computer Science and Engineering, Muthayammal Engineering College, IN
1 Department of Computer Science, Bharathiar University, IN
2 Department of Computer Science and Engineering, Muthayammal Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2384-2389Abstract
Aspect extraction and sentiment identification are the two important tasks to provide effective ischolar_main cause analysis. This work presents a Multi-Level Aspect based Sentiment Analysis (MLASA) model that integrates the aspect extraction and sentiment identification modules to provide effective ischolar_main cause analysis. The aspect extraction module performs token filtration, followed by rule based aspect identification. The heterogeneous multi-level sentiment identification phase performs aspect based sentiment identification. First level performs magnitude along and polarity identification of text, while the second level performs polarity identification using multiple machine learning models. The results are aggregated and ranked based on aspect significance and sentiment magnitude. Experiments and comparisons show effective performance of the MLASA model.Keywords
Root Cause Analysis, Sentiment Identification, Aspect Extraction, Machine Learning, Heterogeneous Modelling,
References
- Soufian Jebbara and Philipp Cimiano, “Aspect-Based Sentiment Analysis using a Two-Step Neural Network Architecture”, Proceedings of International Conference on Semantic Web Evaluation Challenge, pp. 1-8, 2016.
- Ganpat Singh and Yogesh Kumar Meena, “YouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback”, Proceedings of International Conference on Soft Computing and Signal Processing, pp. 63-71, 2019.
- B. Liu, “Sentiment Analysis and Opinion Mining”, Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1, pp. 160-167, 2012.
- Bo Pang and Lillian Lee, “Opinion Mining and Sentiment Analysis”, Computational Linguistics, Vol. 35, No. 2, pp. 311-312, 2009.
- Kim Schouten and Flavius Frasincar, “Survey on Aspect-Level Sentiment Analysis”, IEEE Transactions on Knowledge and Data, Vol. 28, No. 3, pp. 813-830, 2015.
- P.D. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews”, Proceedings of International Annual Meeting on Association for Computational Linguistics, pp. 417-424, 2002.
- Lillian Lee and Shivakumar Vaithyanathan, “Thumbs Up? Sentiment Classification using Machine Learning Techniques”, Proceedings of International Conference on Empirical Methods in Natural Language Processing, pp. 79-86, 2002.
- M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos and S. Manandhar, “Semeval-2016 Task 4: Aspect based Sentiment Analysis”, Proceedings of International Workshop on Semantic Evaluation, pp. 45-56, 2015.
- Tarun Garg and Asif Ekbal, “Multi-Task Learning for Aspect Term Extraction and Aspect Sentiment Classification”, Neurocomputing, Vol. 398, pp. 247-256, 2020.
- Soo Min and Eduard Hovy, “Determining the Sentiment of Opinions”, Proceedings of International Conference on Computational Linguistics, pp. 451-455, 2004.
- Gayatree Ganu, Noemie Elhadad and Amelie Marian, “Beyond the Stars: Improving Rating Predictions using Review Text Content”, Proceedings of International Workshop on the Web and Databases, pp. 224-234, 2009.
- Ana-Maria and Orena Etzioni, “Extracting Product Features and Opinions from Reviews”, Proceedings of International Conference on Natural Language Processing and Text Mining, pp. 9-28, 2007.
- Soujanya Poria, Erik Cambria and Alexander Gelbukh, “Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network”, Knowledge Based Systems, Vol. 108, pp. 42-49, 2016.
- T.A. Rana and Y.N. Cheah, “A Two-Fold Rule-Based Model for Aspect Extraction”, Expert Systems with Applications, Vol. 89, pp. 273-285, 2017.
- Y. Hu, J. Boyd Graber, B. Satinoff and A. Smith, “Interactive Topic Modeling”, Machine Learning, Vol. 95, No. 3, pp. 423-469, 2014.
- Z. Chen and B. Liu, “Mining Topics in Documents: Standing on the Shoulders of Big Data”, Proceedings of 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116-1125, 2014.
- Fangzhao Wu, Sixing Wu, Zhigang Yuan and Yongfeng Huang, “A Hybrid Unsupervised Method for Aspect Term and Opinion Target Extraction”, Knowledge Based Systems, Vol. 148, pp. 66-73, 2018.
- M. Hu and B. Liu, “Mining Opinion Features in Customer Reviews”, Association for the Advancement of Artificial Intelligence, Vol. 4, No. 4, pp. 755-760. 2004.
- Bing Liu, Jiajun Bu and Chun Chen, “Opinion Word Expansion and Target Extraction through Double Propagation”, Computational Linguistics, Vol. 37, No. 1, pp. 1-9, 2011.
- Blair Goldensohn, Kerry Hannan, Ryan McDonald, Tyler Neylon, George Reis and Jeff Reynar, “Building a Sentiment Summarizer for Local Service Reviews”, Proceedings of International Conference on NLP in the Information Explosion Era, pp. 1-8, 2008.
- Z. Toh and W. Wang, “Dlirec: Aspect Term Extraction and Term Polarity Classification System”, Proceedings of International Conference on Computational Linguistics, pp. 267-275, 2014.
- Shanshan Huang, and Kenny Q. Zhu, “Knowledge Empowered Prominent Aspect Extraction from Product Reviews”, Information Processing and Management, Vol. 56, No. 3, pp. 408-423, 2019.
- Manju Venugopalan and Deepa Gupta, “An Unsupervised Hierarchical Rule Based Model for Aspect Term Extraction Augmented with Pruning Strategies”, Procedia Computer Science, Vol. 171, pp. 22-31, 2020.
- Yogesh Kumar Meena, Dinesh Gopalani and Ravi Nahta, “A Two-Step Hybrid Unsupervised Model with Attention Mechanism for Aspect Extraction”, Expert Systems with Applications, Vol. 161, pp. 1-18, 2020.
- Dimitrios Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al Smadi and Mahmoud Al-Ayyoub, “Semeval-2016 Task 5: Aspect Based Sentiment Analysis”, Proceedings of International Workshop on Semantic Evaluation, pp. 33-42, 2016.
- T.A. Rana and Y.N. Cheah, “Aspect Extraction in Sentiment Analysis: Comparative Analysis and Survey”, Artificial Intelligence Review, Vol. 46, No. 4, pp. 459-483, 2016.
- M. Sham and A.B. Dastjerdi, “Enriched LDA (ELDA): Combination of Latent Dirichlet Allocation with Word Co-Occurrence Analysis for Aspect Extraction”, Expert Systems with Applications, Vol. 80, pp. 136-146, 2017.
- Deepak Gupta, Asif Ekbal and Pushpak Bhattacharyya, “Feature Selection and Ensemble Construction: A Two-Step Method for Aspect based Sentiment Analysis”, Knowledge Based Systems, Vol. 125, pp. 1-17, 2017.