Volume 4 , Issue 1 , PP: 46-66, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
AHMED R. ABAS 1 * , IBRAHIM EL-HENAWY 2 , AMR ABDELLATIF 3 , HOSSAM MOHAMED 4
Doi: https://doi.org/10.54216/JCIM.040104
The the task of Aspect-based opinion mining (AbOM) is an emeraging research area, where aspects are mined, the corresponding opinion are scrutinized and sentiments are continuously changed, is gaining increased attention with growing feedback of clients and community across various social media streams. The gigantic improvements of deep learning (DL) techniques in natural language processing (NLP) tasks motivated research community to introduce a novel DL models and for AbSA, each investigate a diverse research points from different perspective, that cope with imminent problems and composite circumstances of AbOM. Consequently, in this survey paper, we concentrate on the limitations of the current studies and challenges relevant to mining of various aspects and their pertinent opinion, interrelationship delineations among different aspects, interactions, dependencies and contextual-semantic associations among various entities for enhanced opinion precision, and estimation of the automaticity of opinion polrity development. A laborious investigation of the later advancement is discussed depending on their contribution in the direction of spotlighting and alleviating the shortcomings related to Aspect Extraction (AE), AbOM, opinion progression (OP). The reported performance for each scrutinized study of Aspect Extraction and Aspect opinion Analysis is also given, revealing the numeriacal evaluation of the presented approach. Future research trends are introduce and delibrated by critically analysing the existing recent approaches, that will be supportive for researchers and advantageous for refining aspect based opinion classification.
Sentiment Analysis, Opinion mining, , Deepl Learning
[1]. E. Cambria, S. Poria, A. Gelbukh, and M. Thelwall, "Sentiment Analysis is a Big Suitcase," IEEE Intelligent Systems, vol. 32, no. 6, pp. 74-80, 2017.
[2]. B. Liu, Sentiment Analysis and Opinion Mining (Synthesis Lectures on Human Language Technologies). Morgan & Claypool Publishers, 2012.
[3]. K. Dave, S. Lawrence, and D.M. Pennock, "Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews," Proc. 12th Int’l Conf. World Wide Web, pp. 519- 528, 2003.
[4]. A. Tripathy, A. Anand, and S.K. Rath, "Document-level Sentiment Classification using Hybrid Machine Learning Approach," Knowledge Information Systems, vol. 53, no. 3, pp. 805- 831, 2017.
[5]. B. Liu, "Sentiment Analysis and Subjectivity," Handbook of Natural Language Processing, pp. 627-666, 2010.
[6]. K. Schouten and F. Frasincar, "Survey on Aspect-Level Sentiment Analysis," IEEE Trans. Knowledge and Data Eng., vol. 28, no. 3, pp. 813-830, 2016.
[7]. J. Wang et al., "Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation," Proc. 2015 IEEE Int’l Conf. Data Mining, pp. 429-438, 2015.
[8]. W. Wang, S.J. Pan, D. Dahlmeier, and X. Xiao, "Recursive Neural Conditional Random Fields for Aspect-Based Sentiment Analysis," Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 616–626, 2016.
[9]. Y. Ma, H. Peng, T. Khan, E. Cambria, and A. Hussain, "Sentic LSTM: A Hybrid Network for Targeted Aspect-Based Sentiment Analysis," Cognitive Computation, vol. 10, no. 4, pp. 639-650, 2018.
[10]. H. Luo, T. Li, B. Liu, B. Wang, and H. Unger, "Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation," arXiv Preprint arXiv:1805.07889, p. 12, 2019.
[11]. H. Li and W. Lu, "Learning Latent Sentiment Scopes for EntityLevel Sentiment Analysis," Proc. 31st AAAI Conf. Artificial Intelligence, pp. 3482-3489, 2017.
[12]. M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, "Semeval-2015 Task 12: Aspect Based Sentiment Analysis," Proc. 9th Int’l Workshop on Semantic Evaluation, pp. 486-495, 2015.
[13]. J. Yang, R. Yang, C. Wang, and J. Xie, "Multi-Entity Aspect-Based Sentiment Analysis With Context, Entity and Aspect Memory," Proc. 32nd AAAI Conf. Artificial Intelligence, pp. 6029-6036, 2018.
[14]. Y. Tay, A.T. Luu, and S.C. Hui, "Learning to Attend via WordAspect Associative Fusion for Aspect-based Sentiment Analysis," Proc. 32nd AAAI Conf. Artificial Intelligence, pp. 5956-5963, 2017.
[15]. J. Wang et al., "Aspect Sentiment Classification with both Wordlevel and Clause-level Attention Networks," Proc. 27th Int’l Joint Conf. Artificial Intelligence, pp. 4439-4445, 2018.
[16]. S. Angelidis and M. Lapata, "Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis," Trans. Association for Computational Linguistics, vol. 6, pp. 17-31, 2018.
[17]. C.G.Q. Chi, Z. Ouyang, and X. Xu, "Changing Perceptions and Reasoning Process: Comparison of Residents’ Pre-And PostEvent Attitudes," Annals of Tourism Research, vol. 70, pp. 39-53, 2018.
[18]. T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent Trends in Deep Learning based Natural Language Processing," IEEE Computational intelligence Magazine, vol. 13, no. 3, pp. 55-75, 2018
[19]. L. Lee and B. Pang, "Opinion Mining and Sentiment Analysis," Foundations and Trends® in Information Retrieval, vol. 2, no. 1–2, pp. 1-135, 2008.
[20]. H. F. Tang, S.B. Tan, and X.Q. Cheng, "A Survey on Sentiment Detection of Reviews," Expert Systems with Applications, vol. 36, no. 7, pp. 10760-10773, 2009.
[21]. M. Wiegand, A. Balahur, B. Roth, D. Klakow, and A. Montoyo, "A Survey on The Role of Negation in Sentiment Analysis," Proc. Workshop on Negation and Speculation in Natural Language Processing, pp. 60-68, 2010.
[22]. M. Tsytsarau and T. Palpanas, "Survey on Mining Subjective Data on The Web," Data Mining and Knowledge Discovery, vol. 24, no. 3, pp. 478-514, 2012.
[23]. K. Ravi and V. Ravi, "A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications," Knowledge-Based Systems, vol. 89, pp. 14-46, 2015.
[24]. A. Giachanou and F. Crestani, "Like It or Not: A Survey of Twitter Sentiment Analysis Methods," ACM Computing Surveys, vol. 49, no. 2, pp. 1-41, 2016.
[25]. L. Zhang, S. Wang, and B. Liu, "Deep Learning for Sentiment Analysis: A Survey," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1253, 2018.
[26]. D. Zimbra, A. Abbasi, D. Zeng, and H. Chen, "The State-of-theArt in Twitter Sentiment Analysis: A Review and Benchmark Evaluation," ACM Trans. Management Information Systems, vol. 9, no. 2, pp. 1-29, 2018.
[27]. L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, "A Survey of Sentiment Analysis in Social Media," Knowledge and Information Systems, pp. 1-47, 2018.
[28]. E. Kim and R. Klinger, "A Survey on Sentiment and Emotion Analysis for Computational Literary Studies," arXiv preprint arXiv:1808.03137, p. 45, 2018.
[29]. A. Vaswani et al., "Attention is All You Need," Proc. 31st Conf. Neural Information Processing Systems, pp. 5998-6008, 2017.
[30]. D. Hu, "An Introductory Survey on Attention Mechanisms in NLP Problems," Proc. SAI Intelligent Systems Conf., pp. 432-448, 2019.
[31]. Y. Ma and J.C. Principe, "A Taxonomy for Neural Memory Networks," IEEE Trans. Neural Networks and Learning Systems, 2019.
[32]. A. S. Manek, P.D. Shenoy, M.C. Mohan, and K. Venugopal, "Aspect Term Extraction for Sentiment Analysis in Large Movie Reviews using Gini Index Feature Selection Method and SVM Classifier," World Wide Web, vol. 20, no. 2, pp. 135-154, 2017.
[33]. T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space," Proc. 2013 Int’l Conf. Learning Representations pp. 1-12, 2013.
[34]. J. Pennington, R. Socher, and C. Manning, "Glove: Global Vectors for Word Representation," Proc. 2014 Conf. Empirical Methods in Natural Language Processing, pp. 1532-1543, 2014.
[35]. M. Peters et al., "Deep Contextualized Word Representations," Proc. 2018 Conf. North American Chapter of the ACL: Human Language Technologies, pp. 2227-2237, 2018.
[36]. J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, "Bert: PreTraining of Deep Bidirectional Transformers for Language Understanding," arXiv preprint arXiv:1810.04805, p. 13, 2018.
[37]. Y. Yin, F. Wei, L. Dong, K. Xu, M. Zhang, and M. Zhou, "Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction," Proc. 25th Int’l Joint Conf. Artificial Intelligence, pp. 2979-2985, 2016.
[38]. P. Liu, S. Joty, and H. Meng, "Fine-Grained Opinion Mining with Recurrent Neural Networks and Word Embeddings," Proc. 2015 Conf. Empirical Methods in Natural Language Processing, pp. 1433- 1443, 2015.
[39]. A. Giannakopoulos, C. Musat, A. Hossmann, and M. Baeriswyl, "Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets," Proc. 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 180–188, 2017.
[40]. T.A. Rana and Y.N. Cheah, "Sequential Patterns Rule-Based Approach for Opinion Target Extraction from Customer Reviews," J. Information Science, p. 0165551518808195, 2018.
[41]. S. Jebbara and P. Cimiano, "Improving Opinion-Target Extraction with Character-Level Word Embeddings," Proc. 1st Workshop on Subword and Character Level Models in NLP, Association for Computational Linguistics, pp. 159–167, 2017.
[42]. Z. Toh and J. Su, "Nlangp at Semeval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features," Proc. 10th Int’l Workshop on Semantic Evaluation, pp. 282-288, 2016.
[43]. R. Agerri and G. Rigau, "Language Independent Sequence Labelling for Opinion Target Extraction," Artificial Intelligence Review, vol. 268, pp. 85-95, 2019.
[44]. D. Ma, S. Li, and H. Wang, "Joint Learning for Targeted Sentiment Analysis," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 4737-4742, 2018.
[45]. X. Li and W. Lam, "Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction," Proc. 2017 Conf. Empirical Methods in Natural Language Processing, pp. 2886-2892, 2017.
[46]. R. He, W.S. Lee, H.T.Ng, and D. Dahlmeier, "An Unsupervised Neural Attention Model for Aspect Extraction," Proc. 55th Ann. Meeting of the ACL, pp. 388-397, 2017.
[47]. W. Wang, S.J. Pan, and D. Dahlmeier, "Multi-Task Coupled Attentions for Category Specific Aspect and Opinion Terms CoExtraction," Proc. 31st Conf. Neural Information Processing Systems, pp. 1-11, 2017.
[48]. W. Wang, S.J. Pan, D. Dahlmeier, and X. Xiao, "Coupled MultiLayer Attentions for Co-Extraction of Aspect and Opinion Terms," Proc. 31st AAAI Conf. Artificial Intelligence p. 7, 2017.
[49]. X. Li, L. Bing, P. Li, W. Lam, and Z. Yang, "Aspect Term Extraction with History Attention and Selective Transformation," Proc. 27th Int’l Joint Conf. Artificial Intelligence, pp. 4194-4200, 2018.
[50]. S. Angelidis and M. Lapata, "Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 3675–3686, 2018.
[51]. M. Schmitt, S. Steinheber, K. Schreiber, and B. Roth, "Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 1109–1114 2018.
[52]. X. Wang, W. Jiang, and Z. Luo, "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts," Proc. COLING 2016, the 26th Int'l Conf. Computational Linguistics, pp. 2428-2437, 2016.
[53]. X. Gu, Y. Gu, and H. Wu, "Cascaded Convolutional Neural Networks for Aspect-Based Opinion Summary," Neural Processing Letters, vol. 46, no. 2, pp. 581-594, 2017.
[54]. H. Xu, B. Liu, L. Shu, and P. S. Yu, "Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction," Proc. 56th Ann. Meeting of the Association for Computational Linguistics, pp. 592–598, 2018.
[55]. D.H. Pham and A.C. Le, "Exploiting Multiple Word Embeddings and One-Hot Character Vectors for Aspect-Based Sentiment Analysis," Int’l J. Approximate Reasoning, vol. 103, pp. 1-10, 2018.
[56]. Y. Liu, S. Li, X. Zhang, and Z. Sui, "Implicit Discourse Relation Classification via Multi-Task Neural Networks," Proc. 13th AAAI Conf. Artificial Intelligence, pp. 2750-2756, 2016.
[57]. M. Tubishat, N. Idris, and M. Abushariah, "Implicit Aspect Extraction in Sentiment Analysis: Review, Taxonomy, Oppportunities, and Open Challenges," Information Processing and Management, vol. 54, no. 4, pp. 545-563, 2018.
[58]. I. Cruz, A. F. Gelbukh, and G. Sidorov, "Implicit Aspect Indicator Extraction for Aspect based Opinion Mining," Int’l J. Computing Linguistics and Applications, vol. 5, no. 2, pp. 135-152, 2014.
[59]. S. Chatterji, N. Varshney, and R. K. Rahul, "AspectFrameNet: A Framenet Extension for Analysis of Sentiments Around Product Aspects," The J. Supercomputing, vol. 73, no. 3, pp. 961-972, 2017.
[60]. T. Gaillat, B. Stearns, R. McDermott, G. Sridhar, M. Zarrouk, and B. Davis, "Implicit and Explicit Aspect Extraction in Financial Microblogs," Ann. Meeting of the Association for Computational Linguistics, 2018.
[61]. S. Poria, I. Chaturvedi, E. Cambria, and F. Bisio, "Sentic LDA: Improving on LDA with Semantic Similarity for Aspect-Based Sentiment Analysis," Int'l Joint Conf. Neural Networks, pp. 4465- 4473, 2016.
[62]. T.A. Rana and Y.N. Cheah, "Aspect Extraction in Sentiment Analysis: A Comparative Analysis and Survey," Artificial Intelligence Review, vol. 46, no. 4, pp. 459-483, 2016.
[63]. Y. Wang, M. Huang, and L. Zhao, "Attention-Based LSTM for Aspect-Level Sentiment Classification," Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 606-615, 2016.
[64]. M. S. Akhtar, D. Gupta, A. Ekbal, and P. Bhattacharyya, "Feature Selection and Ensemble Construction: A Two-Step Method for Aspect Based Sentiment Analysis," Knowledge-Based Systems, vol. 125, pp. 116-135, 2017.
[65]. C. Wu, F. Wu, S. Wu, Z. Yuan, and Y. Huang, "A Hybrid Unsupervised Method for Aspect Term and Opinion Target Extraction," Knowledge-Based Systems, vol. 148, pp. 66-73, 2018.
[66]. S. Deng, A.P. Sinha, and H. Zhao, "Adapting Sentiment Lexicons to Domain-Specific Social Media Texts," Decision Support Systems, vol. 94, no. C, pp. 65-76, 2017.
[67]. 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.
[68]. P. Zhang, J. Wang, Y. Wang, and Y. Wang, "A Statistical Approach to Opinion Target Extraction using Domain Relevance," 2016 2nd IEEE Int’l Conf. Computer and Communications, pp. 273-277, 2016.
[69]. L. Luo, X. Wang, S. Hu, C. Wang, Y. Tang, and L. Chen, "Close yet Distinctive Domain Adaptation," arXiv preprint arXiv:.04235, p. 11, 2017.
[70]. X.N. Kong, M.K. Ng, and Z.H. Zhou, "Transductive Multilabel Learning via Label Set Propagation," IEEE Trans. Knowledge and Data Eng., vol. 25, no. 3, pp. 704-719, 2013.
[71]. R.M. Marcacini, R.G. Rossi, I.P. Matsuno, and S.O. Rezende, "Cross-Domain Aspect Extraction for Sentiment Analysis: A Transductive Learning Approach," Decision Support Systems, vol. 114, pp. 70-80, 2018.
[72]. L. Shu, H. Xu, and B. Liu, "Lifelong Learning CRF for Supervised Aspect Extraction," Proc. 55th Ann. Meeting of the Association for Computational Linguistics, pp. 148–154, 2017. Authorized licensed use limited to: University of Melbourne. Downloaded on May 04,2020 at 02:41:25 UTC from IEEE Xplore.
[73]. S. Poria, E. Cambria, and A. Gelbukh, "Aspect Extraction for Opinion Mining with A Deep Convolutional Neural Network," Knowledge-Based Systems, vol. 108, pp. 42-49, 2016.
[74]. M. Al-Smadi, B. Talafha, M. Al-Ayyoub, and Y. Jararweh, "Using Long Short-Term Memory Deep Neural Networks for AspectBased Sentiment Analysis of Arabic Reviews," Int’l J. Machine Learning Cybernetics, pp. 1-13, 2018.
[75]. X. Zhou, X. Wan, and J. Xiao, "CLOpinionMiner: Opinion Target Extraction in a Cross-Language Scenario," IEEE/ACM Trans. Audio, Speech and Language Processing, vol. 23, no. 4, pp. 619-630, 2015.
[76]. T. Alvarez-López, J. Juncal-Martinez, M. Fernández-Gavilanes, E. Costa-Montenegro, and F. J. González-Castano, "Gti At Semeval2016 Task 5: SVM and CRF for Aspect Detection ad Unsupervised Aspect-Based Sentiment Analysis," Proc. 10th Int’l Workshop on Semantic Evaluation, pp. 306-311, 2016.
[77]. S. Jebbara and P. Cimiano, "Zero-Shot Cross-Lingual Opinion Target Extraction," arXiv preprint arXiv:1904.09122, p. 10, 2019.
[78]. K. Schouten, F. Frasincar, and F. De Jong, "Commit-p1wp3: A Cooccurrence Based Approach to Aspect-Level Sentiment Analysis," Proc. 8th Int’l Workshop on Semantic Evaluation, pp. 203- 207, 2014.
[79]. Y. Liu, F. Wei, S. Li, H. Ji, M. Zhou, and H. Wang, "A Dependency-Based Neural Network for Relation Classification," Proc. 53rd Ann. Meeting of the Association for Computational Linguistics and the 7th Int’l Joint Conf. Natural Language Processing (Volume 2: Short Papers), pp. 285-290, 2015.
[80]. W. Maharani, D.H. Widyantoro, and M.L. Khodra, "Aspect Extraction in Customer Reviews using Syntactic Pattern," Procedia Computer Science, vol. 59, pp. 244-253, 2015.
[81]. L. Qiu, "An Opinion Analysis Model for Implicit Aspect Expressions Based on Semantic Ontology," Int’l J. Grid Distributed Computing, vol. 8, no. 5, pp. 165-172, 2015.
[82]. Q. Liu, B. Liu, Y. Zhang, D. S. Kim, and Z. Gao, "Improving Opinion Aspect Extraction using Semantic Similarity and Aspect Associations," Proc. 13th AAAI Conf. Artificial Intelligence, pp. 2986-2992, 2016.
[83]. S. Kim, J. Zhang, Z. Chen, A. Oh, and S. Liu, "A Hierarchical Aspect-Sentiment Model for Online Reviews," Proc. 27th AAAI Conf. Artificial Intelligence, 2013.
[84]. S. Ruder, P. Ghaffari, and J. G. Breslin, "A Hierarchical Model of Reviews for Aspect-Based Sentiment Analysis," Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 999–1005, 2016.
[85]. S. Poria, E. Cambria, L.W. Ku, C. Gui, and A. Gelbukh, "A RuleBased Approach to Aspect Extraction from Product Reviews," Proc. 2nd Workshop on Natural Language Processing for Social Media, pp. 28-37, 2014.
[86]. A. Mukherjee and B. Liu, "Aspect Extraction through SemiSupervised Modeling," Proc. 50th Ann. Meeting of the Association for Computational Linguistics, pp. 339-348, 2012.
[87]. K. Schouten, O. Van Der Weijde, F. Frasincar, and R. Dekker, "Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-Occurrence Data," IEEE Transactions on Cybernetics, vol. 48, no. 4, pp. 1263-1275, 2017.
[88]. S. Manandhar, "SemEval-2014 Task 4: Aspect Based Sentiment Analysis," Proc. 8th Int’l Workshop on Semantic Evaluation, pp. 19- 30, 2014.
[89]. M. Pontiki et al., "SemEval-2016 Task 5: Aspect Based Sentiment Analysis," Proc. 10th Int’l Workshop on Semantic Evaluation, pp. 19- 30, 2016.
[90]. M. Hu and B. Liu, "Mining and summarizing customer reviews," Proc. 10th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 168-177, 2004.
[91]. M. Mitchell, J. Aguilar, T. Wilson, and B. Van Durme, "Open Domain Targeted Sentiment," Proc. 2013 Conf. Empirical Methods in Natural Language Processing, pp. 1643-1654, 2013.
[92]. H. Jangid, S. Singhal, R. R. Shah, and R. Zimmermann, "AspectBased Financial Sentiment Analysis using Deep Learning," Companion Proc. The Web Conference 2018, pp. 1961-1966, 2018.
[93]. M. Saeidi, G. Bouchard, M. Liakata, and S. Riedel, "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods," Proc. COLING 2016, The 26th Int’l Conf. Computational Linguistics, pp. 1546-1556, 2016.
[94]. M. Wojatzki, E. Ruppert, S. Holschneider, T. Zesch, and C. Biemann, "Germeval 2017: Shared Task on Aspect-Based Sentiment in Social Media Customer Feedback," Proc. GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, pp. 22-29, 2017.
[95]. G. Ganu, N. Elhadad, and A. Marian, "Beyond the Stars: Improving Rating Predictions using Review Text Content," Proc. 12th Int’l Workshop on the Web and Databases, pp. 1-6, 2009.
[96]. J. McAuley, J. Leskovec, and D. Jurafsky, "Learning Attitudes and Attributes from Multi-Aspect Reviews," Proc. 2012 IEEE 12th Int’l Conf. Data Mining, pp. 1020-1025, 2012.
[97]. Z. Luo, S. Huang, F.F. Xu, B.Y. Lin, H. Shi, and K. Zhu, "ExtRA: Extracting Prominent Review Aspects from Customer Feedback," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 3477-3486, 2018.
[98]. H. Wang, C. Wang, C. Zhai, and J. Han, "Learning Online Discussion Structures by Conditional Random Fields," Proc. 34th Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 435-444, 2011.
[99]. H. Ye, Z. Yan, Z. Luo, and W. Chao, "Dependency-Tree Based Convolutional Neural Networks for Aspect Term Extraction," Pacific-Asia Conf. Knowledge Discovery and Data Mining, Springer, pp. 350-362, 2017.
[100]. K. Schouten, F. Frasincar, and F. de Jong, "Ontology-enhanced aspect-based sentiment analysis," Int’l Conf. Web Eng., Springer, pp. 302-320, 2017.
[101]. M. Z. Asghar, A. Khan, S.R. Zahra, S. Ahmad, and F.M. Kundi, "Aspect-Based Opinion Mining Framework using Heuristic Patterns," Cluster Computing, pp. 1-19, 2017.
[102]. A. Konjengbam, N. Dewangan, N. Kumar, and M. Singh, "Aspect Ontology based Review Exploration," Electronic Commerce Research and Applications, vol. 30, pp. 62-71, 2018.
[103]. Z. Hai, K. Chang, and J.j. Kim, "Implicit Feature Identification via Co-Occurrence Association Rule Mining," Int’l Conf. Intelligent Text Processing and Computational Linguistics, pp. 393-404, 2011.
[104]. W. Wang, H. Xu, and W. Wan, "Implicit Feature Identification via Hybrid Association Rule Mining," Expert Systems with Applications, vol. 40, no. 9, pp. 3518-3531, 2013.
[105]. Y. Zhang and W. Zhu, "Extracting Implicit Features in Online Customer Reviews for Opinion Mining," Proc. 22nd Int’l Conf. World Wide Web, pp. 103-104, 2013.
[106]. H. Sayyadi and L. Raschid, "A Graph Analytical Approach for Topic Detection," ACM Trans. Internet Technology, vol. 13, no. 2, p. 4, 2013.
[107]. S. de Kok, L. Punt, R. van den Puttelaar, K. Ranta, K. Schouten, and F. Frasincar, "Aggregated Aspect-Based Sentiment Analysis with Ontology Features," Progress in Artificial Intelligence, vol. 7, no. 4, pp. 295-306, 2018.
[108]. ]K. Khan, B. Baharudin, and A. Khan, "Identifying Product Features from Customer Reviews Using Hybrid Patterns," Int’l Arab J. Information Technology, vol. 11, no. 3, pp. 281-286, 2014.
[109]. S. Kiritchenko, X. Zhu, C. Cherry, and S. Mohammad, "NRCCanada-2014: Detecting Aspects and Sentiment in Customer Reviews," Proc. 8th Int’l Workshop on Semantic Evaluation, pp. 437- 442, 2014.
[110]. J. Wagner et al., "Dcu: Aspect-Based Polarity Classification for Semeval Task 4," Proc. 8th Int’l Workshop on Semantic Evaluation, pp. 223-229, 2014.
[111]. L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, "Adaptive Recursive Neural Network for Target-Dependent Twitter Sentiment Classification," Proc. 52nd Ann. Meeting of the Association for Computational Linguistics, pp. 49-54, 2014.
[112]. D.-T. Vo and Y. Zhang, "Target-Dependent Twitter Sentiment Classification with Rich Automatic Features," Twenty-Fourth Int’l Joint Conf. Artificial Intelligence, 2015.
[113]. D. Tang, B. Qin, X. Feng, and T. Liu, "Effective LSTMs for TargetDependent Sentiment Classification," Proc. COLING 2016, the 26th Int’l Conf. Computational Linguistics, pp. 3298-3307, 2015.
[114]. D.H. Pham and A.C. Le, "Learning Multiple Layers of Knowledge Representation for Aspect Based Sentiment Analysis," Data and Knowledge Eng., vol. 114, pp. 26-39, 2018.
[115]. K. Ghasedi and H. Huang, "Sentiment Analysis via Deep Hybrid Textual-Crowd Learning Model," Proc. 32nd AAAI Conf. Artificial Intelligence, pp. 1563-1570, 2018.
[116]. B. Huang and K. Carley, "Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 1091- 1096, 2018
[117]. M. D. Smucker, D. Kulp, and J. Allan, "Dirichlet Mixtures for Query Estimation in Information Retrieval," Center for Intelligent Information Retrieval, Univ. Massachusetts, pp. 1-13, 2005.
[118]. ]W. Xue and T. Li, "Aspect Based Sentiment Analysis with Gated Convolutional Networks," Proc. 56th Ann. Meeting of the Association for Computational Linguistics, pp. 2514–2523, 2018.
[119]. W. Zhao et al., "Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis," IEEE Trans. Knowledge and Data Eng., vol. 30, no. 1, pp. 185-197, 2018.
[120]. X. Li, L. Bing, W. Lam, and B. Shi, "Transformation Networks for Target-Oriented Sentiment Classification," Proc. 56th Ann. Meeting of the Association for Computational Linguistics, pp. 946–956, 2018.
[121]. R. He, W.S. Lee, H.T. Ng, and D. Dahlmeier, "Exploiting Document Knowledge for Aspect-level Sentiment Classification," Proc. 56th Ann. Meeting of the Association for Computational Linguistics, pp. 579–585, 2018.
[122]. P. Ramachandran, P.J. Liu, and Q.V. Le, "Unsupervised Pretraining for Sequence to Sequence Learning," Proc. 2017 Conf. Empirical Methods in Natural Language Processing, pp. 383-391, 2017.
[123]. M.-T. Luong, Q.V. Le, I. Sutskever, O. Vinyals, and L. Kaiser, "Multi-Task Sequence to Sequence Learning," Proc. 2015 Int’l Conf. Learning Representations pp. 1-10, 2015.
[124]. Q. Hu, J. Zhou, Q. Chen, and L. He, "SNNN: Promoting Word Sentiment and Negation in Neural Sentiment Classification," Proc. 32nd AAAI Conf. Artificial Intelligence, pp. 3255-3262, 2018.
[125]. R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier, "Effective Attention Modeling for Aspect-Level Sentiment Classification," Proc. 27th Int’l Conf. Computational Linguistics, pp. 1121-1131, 2018.
[126]. S. Sukhbaatar, J. Weston, and R. Fergus, "End-to-End Memory Networks," Proc. 28th Int’l Conf. Neural Information Processing Systems, pp. 2440-2448, 2015.
[127]. Y. Tay, L. A. Tuan, and S. C. Hui, "Dyadic Memory Networks for Aspect-Based Sentiment Analysis," Proc. 2017 ACM on Conf. Information and Knowledge Management, pp. 107-116, 2017.
[128]. D. Tang, B. Qin, and T. Liu, "Aspect Level Sentiment Classification with Deep Memory Network," Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 214-224, 2016.
[129]. P. Zhu and T. Qian, "Enhanced Aspect Level Sentiment Classification with Auxiliary Memory," Proc. 27th Int’l Conf. Computational Linguistics, pp. 1077-1087, 2018.
[130]. M. Zhang, Y. Zhang, and D. T. Vo, "Neural Networks for Open Domain Targeted Sentiment," Proc. 2015 Conf. Empirical Methods in Natural Language Processing, pp. 612-621, 2015.
[131]. J. Liu and Y. Zhang, "Attention Modeling for Targeted Sentiment," Proc. 15th Conf. European Chapter of the Association for Computational Linguistics, pp. 572-577, 2017.
[132]. S. Zheng and R. Xia, "Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention," arXiv preprint arXiv:.00892, p. 7, 2018.
[133]. P. Chen, Z. Sun, L. Bing, and W. Yang, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis," Proc. 2017 Conf. Empirical Methods in Natural Language Processing, pp. 452- 461, 2017.
[134]. C. Fan, Q. Gao, J. Du, L. Gui, R. Xu, and K.F. Wong, "ConvolutionBased Memory Network for Aspect-based Sentiment Analysis," Proc. 41st Int’l ACM SIGIR Conf. Research & Development in Information Retrieval, pp. 1161-1164, 2018.
[135]. P. Liu, X. Qiu, and X. Huang, "Deep Multi-Task Learning with Shared Memory," Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 118-127, 2016.
[136]. X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y.-Y. Wang, "Representation Learning using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval," Proc. 2015 Conf. North American Chapter of the Association for Computational Linguistics, pp. 912–921, 2015.
[137]. G. Balikas, S. Moura, and M.-R. Amini, "Multitask Learning for Fine-Grained Twitter Sentiment Analysis," Proc. 40th Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 1005-1008, 2017.
[138]. C. Li, X. Guo, and Q. Mei, "Deep Memory Networks for Attitude Identification," Proc. 10th ACM Int’l Conf. Web Search and Data Mining, pp. 671-680, 2017.
[139]. Y. Yin, Y. Song, and M. Zhang, "Document-level Multi-Aspect Sentiment Classification as Machine Comprehension," Proc. 2017 Conf. Empirical Methods in Natural Language Processing, pp. 2044- 2054, 2017.
[140]. M. Yang, W. Tu, J. Wang, F. Xu, and X. Chen, "Attention Based LSTM for Target Dependent Sentiment Classification," Proc. 31st AAAI Conf. Artificial Intelligence, pp. 5013-5014, 2017.
[141]. Q. Liu, H. Zhang, Y. Zeng, Z. Huang, and Z. Wu, "Content Attention Model for Aspect Based Sentiment Analysis," Proc. 2018 Conf. World Wide Web, pp. 1023-1032, 2018.
[142]. S. Gu, L. Zhang, Y. Hou, and Y. Song, "A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis," Proc. 27th Int’l Conf. Computational Linguistics, pp. 774- 784, 2018.
[143]. N. Majumder, S. Poria, A. Gelbukh, M. S. Akhtar, E. Cambria, and A. Ekbal, "IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 3402-3411, 2018.
[144]. B. Wang and W. Lu, "Learning Latent Opinions for Aspect-level Sentiment Classification," Proc. 32nd AAAI Conf. Artificial Intelligence, pp. 5537-5544, 2018.
[145]. D. Hazarika, S. Poria, P. Vij, G. Krishnamurthy, E. Cambria, and R. Zimmermann, "Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis," Proc. Conf. North American Chapter of the Association for Computational Linguistics, pp. 266- 270, 2018.
[146]. P. Lin, M. Yang, and J. Lai, "Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Authorized licensed use limited to Sentiment Classification," Proc. 28th Int'l Joint Conf. Artificial Intelligence, pp. 5088-5094, 2019.
[147]. J. Li, H. Yang, and C. Zong, "Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings," Proc. 27th Int’l Conf. Computational Linguistics, pp. 925-936, 2018.
[148]. J. McAuley, R. Pandey, and J. Leskovec, "Inferring Networks of Substitutable and Complementary Products," Proc. 21th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 785-794, 2015.
[149]. B. Pang and L. Lee, "A Sentimental Education: Sentiment Analysis using Subjectivity Summarization based on Minimum Cuts," Proc. 42nd Ann. Meeting on Association for Computational Linguistics, pp. 271-278, 2004.
[150]. H. Wang, Y. Lu, and C. Zhai, "Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach," Proc. 16th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 783-792, 2010.
[151]. M. Yang, Q. Qu, X. Chen, C. Guo, Y. Shen, and K. Lei, "FeatureEnhanced Attention Network for Target-Dependent Sentiment Classification," Neurocomputing, vol. 307, pp. 91-97, 2018.
[152]. M. Zhang, Y. Zhang, and D.-T. Vo, "Gated Neural Networks for Targeted Sentiment Analysis," Proc. 13th AAAI Conf. Artificial Intelligence, pp. 3087-3093, 2016.
[153]. S. Wang, S. Mazumder, B. Liu, M. Zhou, and Y. Chang, "TargetSensitive Memory Networks for Aspect Sentiment Classification," Proc. 56th Ann. Meeting of the Association for Computational Linguistics, pp. 957-967, 2018.
[154]. D. Ma, S. Li, X. Zhang, and H. Wang, "Interactive Attention Networks for Aspect-Level Sentiment Classification," Proc. 76th Int’l Joint Conf. Artificial Intelligence, pp. 4068-4074, 2017.
[155]. X. Ma, J. Zeng, L. Peng, G. Fortino, and Y. Zhang, "Modeling Multi-Aspects within one Opinionated Sentence Simultaneously for Aspect-Level Sentiment Analysis," Future Generation Computer Systems, vol. 93, pp. 304-311, 2019.
[156]. F. Fan, Y. Feng, and D. Zhao, "Multi-Grained Attention Network for Aspect-Level Sentiment Classification," Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 3433-3442, 2018.
[157]. Ma, K. Wang, T. Qiu, A.K. Sangaiah, D. Lin, and H.B. Liaqat, "Feature-based Compositing Memory Networks for AspectBased Sentiment Classification in Social Internet of Things," Future Generation Computer Systems, vol. 92, pp. 879-888, 2017.
[158]. J. Qiu, C. Liu, Y. Li, and Z. Lin, "Leveraging Sentiment Analysis at the Aspects Level to Predict Ratings of Reviews," Information Sciences, vol. 451, pp. 295-309, 2018.
[159]. H.X. Yang, Z.X. Wu, C. Zhou, T. Zhou, and B.-H. Wang, "Effects of Social Diversity on the Emergence of Global Consensus in Opinion Dynamics," Physical Review E, vol. 80, no. 4, p. 046108, 2009.
[160]. J. Aronfreed, "The Socialization of Altruistic and Sympathetic Behavior: Some Theoretical and Experimental Analyses," Altruism and Helping Behavior, pp. 103-126, 1970.
[161]. K. Kaplanidou et al., "Quality of Life, Event Impacts, and MegaEvent Support among South African Residents Before and After The 2010 Fifa World Cup," J. Travel Research, vol. 52, no. 5, pp. 631-645, 2013.
[162]. F. Fu and L. Wang, "Co-evolutionary Dynamics of Opinions and Networks: From Diversity to Uniformity," Physical Review E, vol. 78, no. 1, pp. 0161041-1060414, 2008.
[163]. T. Carletti, S. Righi, and D. Fanelli, "Emerging Structures in Social Networks Guided by Opinions' Exchanges," Advances in Complex Systems, vol. 14, no. 01, pp. 13-30, 2011.
[164]. K. Sznajd-Weron and J. Sznajd, "Opinion Evolution in Closed Community," Int’l J. Modern Physics C, vol. 11, no. 06, pp. 1157- 1165, 2000.
[165]. P. Fan, P. Li, H. Wang, Z. Jiang, and W. Li, "Opinion Interaction Network: Opinion Dynamics in Social Networks with Heterogeneous Relationships," Proc. ACM SIGKDD Workshop on Intelligence and Security Informatics, p. 8, 2012.
[166]. H. Liang, Y. Yang, and X. Wang, "Opinion Dynamics in Networks with Heterogeneous Confidence and Influence," Physica A: Statistical Mechanics and its Applications, vol. 392, no. 9, pp. 2248-2256, 2013.
[167]. L.T. Nguyen, P. Wu, W. Chan, W. Peng, and Y. Zhang, "Predicting Collective Sentiment Dynamics from Time-Series Social Media," Proc. First Int’l Workshop on Issues of Sentiment Discovery and Opinion Mining, p. 8, 2012.
[168]. P.C. Guerra, W. Meira Jr, and C. Cardie, "Sentiment Analysis on Evolving Social Streams: How Self-Report Imbalances can Help," Proc. 7th ACM Int’l Conf. Web Search and Data Mining, pp. 443-452, 2014.
[169]. L. Zhang, Y. Jia, X. Zhu, B. Zhou, and Y. Han, "User-Level Sentiment Evolution Analysis in Microblog," China Communications, vol. 11, no. 12, pp. 152-163, 2014.
[170]. N. Charlton, C. Singleton, and D.V. Greetham, "In the Mood: The Dynamics of Collective Sentiments on Twitter," Royal Society Open Science, vol. 3, no. 6, pp. 160-162, 2016.
[171]. S. Poria, N. Majumder, D. Hazarika, E. Cambria, A. Gelbukh, and A. Hussain, "Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines," IEEE Intelligent Systems, vol. 33, no. 6, pp. 17-25, 2018.
