Abstract
Background: Nowadays, with the appearance of web 2.0, more users express their opinions, judgments, and thoughts towards certain objects, services, organizations, and their attributes via social networking, forum entries, websites, and blogs and so on. In this way, the volume of raw content generated by these users will increase rapidly with enormous size, where people often find difficulties in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional opinion mining techniques, which focused on the overall sentiment of the review, fails to uncover the sentiments expressed on the aspects of the reviewed entity. For that, researchers in Aspect-based opinion mining community try to solve and handle this problem.
Objectives: Our proposed study aims to present, survey and compare in the first place the important recent Aspect-based opinion mining approaches relevant to important languages such English, Arabic and Chinese and commonly datasets used in literature so that future researchers could improve their results. The cited approaches used the last techniques in the area on Opinion mining field, relevant to the Deep Learning models. In the second place, we try to highlight and give special attention to the Arabic language by introducing a dashboard of deep learning methods dedicated to the Arabic language. Finally, we emphasize the research gaps and future challenges in both English and Arabic languages that provide some new potential research fields.
Methods: We have carefully summarized 48 models according to their algorithm into three categories: supervised, semi-supervised and unsupervised. Due to a large number of approaches with diverse datasets and techniques, we propose some statistical graphics to compare different experimentation results namely precision, Recall, and F-measure. Also, the study has conferred a comparative analysis and a comprehensive discussion of different approaches and techniques dedicated to the aspect extraction sub-task using the new tendency that of deep learning on both Arabic, English and Chinese language. We have introduced some future challenges, research gaps, and new trends in the opinion mining task, which need more efforts and investigations to present new solutions that make the opinion mining field more pervasive and give more ideas about the different researches done in the field of OM.
Conclusion: We have compared the different approaches and techniques dedicated to the extraction of aspects using the new tendency that of deep learning. Our contribution illustrates the add values given by deep learning models in the treatment of user reviews expressed in the Arabic language. At the same time, this work is mainly based on the use of the evaluation performance metrics (precision, recall, and f-measure).
Keywords: Sentiment analysis, opinion mining, aspect extraction, deep learning, arabic, opinion mining.
Graphical Abstract
[1]
B. Liu, "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies. . Vol.5, No., pp.1-167,2012.
[2]
Z. Lei, and B. Liu, Sentiment analysis and opinion mining.. San Francisco, CA, USA Univ. of Illinois 2011
[3]
M. Hu, and B. Liu, "Mining and summarizing customer reviews", in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Seattle, Washington 2004, pp. 168-177.
[4]
X. Ding, B. Liu, and P.S. Yu, "A holistic lexicon-based approach to opinion mining", in First ACM International Conference on Web Search and Data Mining (WSDM). Stanford, California, USA:Stanford University, 2008, pp. 231-240. [Online Available:, http://dl.acm.org/citation.cfm?id=1341561]
[5]
M. Hu, and B. Liu, Mining opinion features in customer reviews.AAAI, . Vol. 4, pp. 755-760, 2004.
[6]
A. Esuli, and F. Sebastiani, “Sentiwordnet: A publicly available lexical resource for opinion mining,” LREC., vol. 6. CiteSeer, pp. 417-422. 2006
[7]
C. Strapparava, and A. Valitutti, "Wordnet affect: An effective extension of wordnet", LREC, vol. 4, pp. 1083-1086, 2004.
[8]
B. Liu, M. Hu, and J. Cheng, "Opinion observer: Analyzing and comparing opinions on the web", in 14th International Conference on World Wide Web ACM. 2005, pp. 342-351
[9]
R. Feldman, "Techniques and applications for sentiment analysis", Commun. ACM, vol. 56, no. 4, pp. 82-89, 2013.
[10]
A. Popescu, and O. Etzioni, Extracting product features and opinions from reviews., EMNLP, pp. 339-346. 2005
[12]
Z. Lei, L. Bing, L.S. Hwan, and O. Eamonn, Extracting and ranking product features in opinion documents., COLING, pp. 1462-1470. 2010
[13]
Q. Guang, L. Bing, B. Jiajun, and C. Chun, "Opinion word expan- sion and target extraction through double propagation", Comput. Linguist., vol. 37, no. 1, pp. 9-27, 2011.
[14]
L. Fangtao, H. Chao, H. Minlie, Z. Xiaoyan, X. Ying-Ju, Z. Shu, and Y. Hao, "Structure-aware review mining", In: Proceedings of the 23rd International Conference on Computational Linguistics. Beijing, August 2010, pp. 653-661
[15]
N. Jakob, and I. Gurevych, “Extracting opinion targets in a single- and cross-domain setting with conditional random fields,” EMNLP-2010., ACL, 2010, pp. 1035-1045.
[16]
T. Zhiqiang, and W. Wenting, "DLIREC: Aspect term extraction and term polarity classification system", The 8th International Workshop on Semantic Evaluation (SemEval 2014),. 2014, pp.235-240.
[17]
S. Poria, E. Cambria, A. Gelbukh, F. Bisio, and A. Hussain, "Sentiment data flow analysis by means of dynamic linguistic patterns", IEEE Comput. Intell. Mag., vol. 10, no. 4, pp. 26-36, 2015.
[18]
S. Ruder, P. Ghaffari, and G.J. Breslin, "A hierarchical model of reviews for aspect-based sentiment analysis", The 2016 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. 2016, pp. 999-1005.
[19]
M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, "Deep recurrent neural network vs. support vector machine for aspect- based sentiment analysis of Arabic hotels’ reviews", J. Computational Sci, pp. 386-393, 2017.
[20]
"M. AL-Smadi, B. Talafha, M. Al-Ayyoub, and Y. Jararweh, “Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews", Int. J. Mach. Learn. Cybern., vol. 10, pp. 2163-2175, 2019.
[http://dx.doi.org/10.1007/s13042-018-0799-4]
[http://dx.doi.org/10.1007/s13042-018-0799-4]
[21]
D. Li, and D. Yu, "Deep learning: Methods and applications", in Found. Trends Sign. Process. . Vol. 7, No. (3-4), 2014, pp.197-387.
[22]
H. Do, P. Prasad, A. Maag, and A. Alsadoon, "Deep learning for aspect-based sentiment analysis: A comparative review", Expert Syst. Appl., vol. 118, pp. 272-299, 2018.
[23]
M. Tubishat, N. Idris, and M.A.M. Abushariah, "Implicit aspect extraction in sentiment analysis: Review, taxonomy, opportunities, and open challenges", Inf. Process. Manage., vol. 54, no. 4, pp. 545-563, 2018.
[24]
K. Schouten, and F. Frasincar, "Survey on aspect-level sentiment analysis", IEEE Trans. Knowl. Data Eng., vol. 28, no. 3, pp. 813-830, 2016. [http://dx.doi.org/10.1109/TKDE.2015.2485209].
[25]
R.J. Kreuz, and S. Glucksberg, "How to be sarcastic: The echoic reminder theory of verbal irony", J. Exp. Psychol. Gen., vol. 118, no. 4, p. 374, 1989.
[26]
F. Cruz, J. Troyano, F. Enrquez, F. Ortega, and C. Vallejo, "Long autonomy or long delay?’The importance of domain in opinion mining", Expert Syst. Appl., vol. 40, no. 8, pp. 3174-3174, 2013.
[27]
S. Huang, X. Liu, X. Peng, and Z. Niu, "Fine-grained product features extraction and categorization in reviews opinion mining", in 12th international conference on IEEE data mining workshops (ICDMW). IEEE, . 2012, pp.680-686.
[28]
B. Yang, and C. Cardie, "Joint inference for fine-grained opinion extraction", ACL. Vol. 1, 2013, pp. 1640-1649.
[29]
S. Li, R. Wang, and G. Zhou, "Opinion target extraction using a shallow semantic parsing framework", Twenty-sixth AAAI Conference on Artificial Intelligence. 2012, pp. 1671-1677
[30]
A. Graves, A. Mohamed, and G. Hinton, "Speech recognition with deep recurrent neural networks", IEEE International Conference on Acoustics, Speech and Signal Processing. 2013, pp. 6645-6649
[31]
Y. Kim, "Convolutional neural networks for sentence classification", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014. 2014, pp. 1746-1751.[Available at: arXivpreprintarXiv:1408.5882]
[32]
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for imagerecognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, pp. 770-778
[33]
S. Shiliang, L. Chen, and J. Chen, "A review of natural language processing techniques for opinion mining systems", Inf. Fusion, vol. 36, pp. 10-25, 2017.
[34]
G. Lample, M. Balles-teros, S. Subramanian, K. Kawakami, and C. Dyer, "Neural architectures for named entity recognition", Proceedings of NAACL.San Diego, California, USA June 2016.
[35]
W. Ling, C. Dyer, A. Black, I. Trancoso, R. Fernandez, S. Amir, L. Marujo, and T. Luis, "“Finding function in the form: Compositional character models for open-vocabulary word representation", in The Proceeding of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal. 2015, pp. 1520-1530
[36]
M. Jabreel, F. Hassan, and A. Moreno, "Target-dependent sentiment analysis of tweets using bidirectional gated recurrent neural networkS", In: Advances in Hybridization of Intelligent Methods. Smart Innovation, Systems and Technologies. I. Hatzilygeroudis and V. Palade, EdsSpringer: Heidelberg, Berlin, . Vol. 85. 2018, pp. 39-55.
[37]
R. Socher, A. Perelygin, Y. Wu, J. Chuang, C.D. Manning, Y.N. Andrew, and C. Potts, "Recursive deep models for semantic compo- sitionality over a sentiment treebank", in The Conference On Empirical Methods in Natural Language Processing.EMNLP, . 2013, pp. 1631- 1642
[38]
H. Lakkaraju, R. Socher, and C. Manning, "Aspect specific sentiment analysis using hierarchical deep learning", Proceedings of the NIPS Workshop on Deep Learning and Representation Learning. 2014, pp. 1-9
[39]
P. Le, and Z. Zuidema, "Compositional distributional semantics with long short-term memory", in Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics (*SEM 2015). Denver, Colorado: SEM Organizing Committee, June 4-5,2015, pp. 10-19. [Available at: arXiv:1503.02510]
[40]
K. Liu, L. Xu, and J. Zhao, "Co-extracting opinion targets and opinion words from online reviews based on the word alignment model", IEEE Trans. Knowl. Data Eng., vol. 27, no. 3, pp. 636-650, 2015.
[41]
Y. Yin, F. Wei, D. Li, K. Xu, M. Zhang, and M. Zhou, Unsupervised word and dependency path embeddings for aspect term extractionin IJCAI. 2016, pp. 2979-2985. [Available Online:arxiv.org/pdf/1605.07843]
[42]
W. Wang, J.P. Sinno, D. Dahlmeier, and W. Xiao, "Recursive Neural conditional random fields for aspect-based sentiment analysis", in The Conference on Empirical Methods in Natural Language Processing. Austin, Texas, 2016, pp. 616-626.
[43]
P. Liu, S. Joty, and H. Meng, "Fine-grained opinion mining with recurrent neural networks and word embeddings", in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal, 17-21 September 2015, pp.1433-1443.
[45]
M. Jordan, "Serial order: A parallel distributed processing approach", in Advances in psychology, ICS report 8608, Institute for Cognitive Science, UCSD, La Jolla. 1986. [Available Online:cseweb.ucsd.edu/ÌCˇ gary/PAPER-SUGGESTIONS/Jordan-TR-8604.pdf]
[46]
S. Poria, E. Cambria, and A. Gelbukh, "Aspect extraction for opinion mining with a deep convolutional neural network Knowledge-Based", Knowl. Base. Syst., vol. 108, pp. 42-49, 2016.
[47]
S. Jebbara, and P. Cimiano, "Aspect-based sentiment analysis using a two-step neural network architecture", in The European Semantic Web Conference (ESWC). 2016, pp. 153-167
[48]
M. Jabreel, F. Hassan, and A. Moreno, "Target-Dependent sentiment analysis of tweets using bidirectional gated recurrent neural networks", in Advances in Hybridization of Intelligent Methods. Smart Innovation, Systems and Technologies.. I. Hatzilygeroudis and V. Palade, Eds., Springer, Heidelberg, Berlin, Vol. 85. 2018,pp. 39-55.
[49]
Z. Hai, K. Chang, and G. Cong, "One seed to find them all: Mining opinion features via association", in The 21st ACM International Conference on Information and Knowledge Management. ACM. 2012, pp. 255-264.
[50]
Q. Zhao, and H. Wang, " P. L. V, and C. Zhang, “A bootstrapping based refinement framework for mining opinion words and targets,”", in The 23rd ACM International Conference on Information and Knowledge Management.. ACM, 2014, pp.1995-1998
[51]
J. Yu, Z.J. Zha, M. Wang, and T.S. Chua, "Aspect ranking: Identifying important product aspects from online consumer reviews", In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language TechnologiesAssociation for Computational Linguistics, . Vol. 1, 2011, pp. 1496-1505
[52]
B. Ma, D. Zhang, Z. Yan, and T. Kim, "An lda and synonym lexicon- based approach to product feature extraction from online consumer product reviews", J. Electron. Commerce Res., vol. 4, pp. 304-314, 2013.
[53]
Z. Yan, M. Xing, D. Zhang, and B. Ma, "B.: An extended PageRank method for product feature extraction from online consumer reviews", Inf. Manage., vol. 52, no. 7, pp. 850-858, 2015.
[54]
K. Liu, L. Xu, Y. Liu, and J. Zhao, "Opinion target extraction using partially-supervised word alignment model", in The Twenty-third International Joint Conference on Artificial Intelligence. New Orleans,AAAI Press, 2013, pp. 2134-2140
[55]
K. Liu, I. Xu, and J. Zhao, "Opinion target extraction using word- based translation model", in The joint conference on empirical methods in natural language processing and computational natural language learning, Association for computational linguistics. 2012,pp. 1346-1356.
[56]
A. Samha, Y. Li, and J. Zhang, Aspect-based opinion extraction from customer reviews. 2014. [Available Online: arXivpreprintarXiv:1404.1982].
[57]
K. Bafna, and D. Toshniwal, "Feature-based summarization of cus- tomers’ Reviews of Online Products", in Procedia Comp Sci, Vol.. 22, pp. 142-151, 2013.
[58]
T. Marrese, J. Velásquez, F. Marquez, and Y. Matsuo, "Identifying customer preferences about tourism products using an aspect-based opinion mining approach", in Procedia Comp Sci. Vol. 22, pp. 182-191, 2013.
[59]
M. Eirinaki, S. Pisal, and J. Singh, "Feature-based opinion mining and ranking", J. Comput. Syst. Sci., vol. 78, no. 4, pp. 1175-1184, 2012.
[60]
T. Marrese, J. Velásquez, and F. Marquez, "A novel deterministic approach for aspect-based opinion mining in tourism products reviews", Expert Syst. Appl., vol. 41, no. 17, pp. 7764-7775, 2014.
[61]
T. Marrese, J. Velasquez, and F. Marquez, "Opinion zoom: A modular tool to explore tourism opinions on the web", in IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). Vol. 3, 2013, pp. 261-264.
[62]
A. Bagheri, M. Saraee, and F. Jong, "An unsupervised aspect detection model for sentiment analysis of reviews", in NLDB. Vol.7934. Springer, Heidelberg, Berlin, pp. 140-151, 2013
[63]
Y. Li, H. Wang, Q. Qin, W. Xu, and J. Guo, "Confidence estimation and reputation analysis in aspect extraction", in The 22nd international conference on IEEE pattern recognition (ICPR). 2014,pp. 3612-3617
[64]
Y. Li, Z. Qin, W. Xu, and J. Guo, "A holistic model of mining product aspects and associated sentiments from online reviews", Multimedia Tools Appl., vol. 74, no. 23, pp. 10177-10194, 2015.
[65]
W. Bancken, D. Alfarone, and J. Davis, "Automatically detecting and rating product aspects from textual customer reviews", in Proceedings of DMNLP Workshop at ECML/PKDD. 2014, pp.1-16
[66]
S. Poria, E. Cambria, L. Ku, C. Gui, and A. Gelbukh, "A rule-based approach to aspect extraction from product reviews", in The Second Workshop on Natural Language Processing for Social Media (SocialNLP). 2014, pp.28-37
[67]
J. Du, W. Chan, and X. Zhou, "A product aspects identification method by using a translation-based language model", The 22nd International Conference on IEEE Pattern Recognition (ICPR),. 2014, pp. 2790-2795.
[68]
Z. Hai, K. Chang, J. Kim, and C. Yang, "Identifying features in opinion mining via intrinsic and extrinsic domain relevance", IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 623-634, 2014.
[69]
C. Quan, and F. Ren, "Unsupervised product feature extraction for feature-oriented opinion determination", Inf. Syst., vol. 272, pp. 16-28, 2014.
[70]
L. Qian, L. Bing, Y. Zhang, D. Kim, and G. Zhiqiang, "Improving Opinion Aspect Extraction Using Semantic Similarity and Aspect As- sociations", The Thirtieth AAAI Conference on Artificial Intelligence. Barcelona, Spain, 2016, pp. 2986-2992.
[71]
M. Duwairi, and I. Qarqaz, "Arabic Sentiment Analysis usingSupervised Classification", in The 1st International Workshop on Social Networks Analysis, Management and Security (SNAMS),. Barcelona, Spain, 2014.
[72]
A. Assiri, A. Emam, and A. Aldossar, "Arabic sentiment analysis: A survey", IJACSA. Vol.6, No.12, 2015
[73]
D. Bouras, H. Bendjanna, and M. Amroun, "Opinion Mining State of the art", in The 3rd International Conference on Networking andAdvanced Systems. Annaba, Algeria, 2017.
[74]
N. Boudad, R. Faizi, R. Thami, and R. Chiheb, "Sentiment analysis in Arabic: A review of the literature", Ain Shams Engin. J., vol. 9, no. 4, pp. 2479-2490, 2018.
[75]
M. Al-Smadi, I. Obaidat, M. Irbid, R. Mohawesh, and Y. Jararweh, "Using enhanced lexicon-based approaches for the determination of aspect categories and their polarities in arabic reviews", IJITWE, vol. 11, no. 3, pp. 15-31, 2016.
[76]
M. Alhazmi, and N. Salim, "Arabic opinion target extraction from tweets", ARPN J. Engin. Appl. Sci., vol. 10, no. 3, pp. 1023-1026, 2015.
[77]
A. Hassan, and A. Abu-Jbara, "Detecting subgroups in online discussions by modeling positive and negative relations among participants", The 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics. 2012,pp. 59-70.
[78]
"M. Elarnaoty, S. Abdel-Rahman, and A. Fahmy, “A Machine Learning Approach for Opinion Holder Extraction,”", Info. Retriev. ISSN: 0976-2191, Vol. 3, 2012. [Available at: arXiv:1206.1011]
[79]
M. Al-Smadi, M. Al-Ayyoub, H. Al-Sarhan, and Y. Jararweh, "An Aspect-Based Sentiment Analysis Approach to Evaluating Arabic News Affect on Readers", J. Univers. Comput. Sci., vol. 22, no. 5, pp. 630-649, 2016.
[80]
L. Abd-Elhamid, D. Elzanfaly, and A. Eldin, "Feature-based sentiment analysis in online Arabic reviews", The 11th International Conference on Computer Engineering Systems (ICCES),. 2016, pp. 260-265.
[81]
"Shimaa, A. Alsammak, and T. Elshishtawy, “A generic approach for extracting aspects and opinions of Arabic reviews,”", in INFOS ’16 Proceedings of the 10th International Conference on Informatics and Systems. Cairo, Egypt, 2016
[82]
"Obaidat, R. Mohawesh, M. Al-Ayyoub, M. AL-Smadi, Y. Jararweh, “Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches", in IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). 2015, pp. 1-6
[83]
M. Al-Smadi, O. Qawasmeh, B. Talafha, and M. Quwaider, "Human annotated Arabic dataset of book reviews for aspect-based sentiment analysis", in The 3rd International Conference on the Future Internet of Things and Cloud (FiCloud). 2015, pp.726-730
[84]
M. Aly, and A. Atiya, "LABR: a large scale Arabic book reviewsdataset", in The Meetings of the Association for Computational Linguistics.ACL, Vol. 2, pp. 494-498.
[85]
M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, "M. AL-Smadi, and G. Eryiit, “Aspect-based sentiment analysis,", in The 10th International Workshop on Semantic Evaluation. 2016
[86]
N. Farra, K. McKeown, and N. Habash, "Sentiment Models for Arabic Target entities", in The 15th Conference of the European Chapter of the Association for Computational Linguistics EACL. Valencia, Spain, April 2017, pp. 1002-1013
[87]
M. Al-Smadi, B. Talafha, M. Al-Ayyoub, and Y. And, "Jararweh, “using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews", Int. J. Mach. Learn. Cybern., vol. 10, pp. 2163-2175, 2019. [Available Online: 10.1007/S13042-018-0799-4].
[88]
S. Shahid, N. Jaafar, S. Bahri, A. Sulaiman, P.S. Wai, Y. Chung, A. Piprani, and M. Al-garadi, "Sentiment analysis of big data: methods, applications, and open challenges,” IEEE Access", Vol. 6,pp. 37807-37827, 2018. [Online]. Available:
[http://dx.doi.org/10.1109/ACCESS.2018.2851311]
[http://dx.doi.org/10.1109/ACCESS.2018.2851311]
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