A Study on Diverse Methods and Performance Measures in Sentiment Analysis

Meesala    Shobha    Rani; Subramanian      Sumathy
Abstract

With the vast development of Internet technology 2.0, millions of people share their opinions on different social networking sites. To obtain the necessary information from the large volume of user-generated data, the attention on sentiment analysis among the research community is growing. The growth and prominence of sentiment analysis are synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of usergenerated content such as noisy, irrelevant information and fake reviews, there is a significant demand for a practical methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies. It highlights the challenges and performance factors of various sentiment analysis approaches, including text preprocessing, opinion spam detection, and aspect level sentiment analysis.
Users use social media as a medium for their activities and are passionate about their posts on social networking platforms on various issues, topics, and events. Sentiment analysis plays a significant role in online e-commerce servicing sites in which users share their views and rating on products and services. With the help of sentiment analysis, companies identify customer dissatisfaction and enhance the quality of the products and services.
This study seeks diverse methods and performance measures on various application domains in sentiment analysis.
The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis, namely text preprocessing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning.
User-generated content is growing worldwide, and people more eagerly express their views on social media towards various aspects. The opinionated text is challenging to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the user-generated data. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.
Keywords: Sentiment analysis, machine learning, lexicon based, hybrid based, opinion spam detection, text pre-processing, aspect based sentiment analysis.
Graphical Abstract

[1] 
F.H. Khan, U. Qamar,  and S. Bashir, "Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio", Artif. Intell. Rev., vol. 48, pp. 113-138, 2017.
[http://dx.doi.org/10.1007/s10462-016-9496-4] 
[2] 
B. Liu, Sentiment analysis and opinion mining., Synthesis Lectures on Human Language Technologies, 2012, pp. 1-184.
[http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016] 
[3] 
M.S. Rani,  and S. Sumathy, "Analysis on various machine learning based approaches with a perspective on the performance", Innov. Power Adv. Comput. Technol. i-PACT, 2017.
[4] 
K. Ravi,  and V. Ravi, "PRE - PRINT VERSION A survey on opinion mining and sentiment analysis : tasks, approaches and applications PRE - PRINT VERSION. Knowl. -", Based Syst., vol. 89, pp. 14-46, 2015.
[http://dx.doi.org/10.1016/j.knosys.2015.06.015] 
[5] 
M.S. Rani,  and S. Sumathy, "Perspectives of the performance metrics in lexicon and hybrid based approaches: A review", Int. J. Eng. Technol., vol. 6, pp. 108-115, 2017.
[http://dx.doi.org/10.14419/ijet.v6i4.8295] 
[6] 
C.H. Li, J.C. Yang,  and S.C. Park, "Text categorization algorithms using semantic approaches, corpus-based thesaurus and WordNet", Expert Syst. Appl., vol. 39, pp. 765-772, 2012.
[http://dx.doi.org/10.1016/j.eswa.2011.07.070] 
[7] 
Y. Ren,  and D. Ji, "Learning to Detect Deceptive Opinion Spam: A Survey", IEEE Access, vol. 7, pp. 42934-42945, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2908495] 
[8] 
B. Tang, S. Kay,  and H. He, "Toward Optimal Feature Selection in Naive Bayes for Text Categorization", IEEE Trans. Knowl. Data Eng., vol. 28, pp. 2508-2521, 2016.
[http://dx.doi.org/10.1109/TKDE.2016.2563436] 
[9] 
R. Hu, B. Mac Namee,  and S.J. Delany, "Active learning for text classification with reusability", Expert Syst. Appl., vol. 45, pp. 438-449, 2016.
[http://dx.doi.org/10.1016/j.eswa.2015.10.003] 
[10] 
N. Mohd Sharef,  and T. Martin, "Evolving fuzzy grammar for crime texts categorization", Appl. Soft Comput. J., vol. 28, pp. 175-187, 2015.
[http://dx.doi.org/10.1016/j.asoc.2014.11.038] 
[11] 
A.S.H. Basari, B. Hussin, I.G.P. Ananta,  and J. Zeniarja, "Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization", Procedia Eng., vol. 53, pp. 453-462, 2013.
[http://dx.doi.org/10.1016/j.proeng.2013.02.059] 
[12] 
A. Onan, S. Korukoğlu,  and H. Bulut, "A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification", Expert Syst. Appl., vol. 62, pp. 1-16, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.06.005] 
[13] 
B. Ramesh,  and J.G.R. Sathiaseelan, "An Advanced Multi Class Instance Selection based Support Vector Machine for Text Classification", Procedia Comput. Sci., vol. 57, pp. 1124-1130, 2015.
[http://dx.doi.org/10.1016/j.procs.2015.07.400] 
[14] 
L. Jiang, C. Li, S. Wang,  and L. Zhang, "Deep feature weighting for naive Bayes and its application to text classification", Eng. Appl. Artif. Intell., vol. 52, pp. 26-39, 2016.
[http://dx.doi.org/10.1016/j.engappai.2016.02.002] 
[15] 
A.S. Ghareb, A.A. Bakar,  and A.R. Hamdan, "Hybrid feature selection based on enhanced genetic algorithm for text categorization", Expert Syst. Appl., vol. 49, pp. 31-47, 2016.
[http://dx.doi.org/10.1016/j.eswa.2015.12.004] 
[16] 
A. Tripathy, A. Agrawal,  and S.K. Rath, "Classification of Sentimental Reviews Using Machine Learning Techniques", Procedia Comput. Sci., vol. 57, pp. 821-829, 2015.
[http://dx.doi.org/10.1016/j.procs.2015.07.523] 
[17] 
J.H. Lee, W.C. Yeh,  and M.C. Chuang, "Web page classification based on a simplified swarm optimization", Appl. Math. Comput., vol. 270, pp. 13-24, 2015.
[http://dx.doi.org/10.1016/j.amc.2015.07.120] 
[18] 
N. Shafiabady, L.H.H. Lee, R. Rajkumar, V.P.P. Kallimani, N.A. Akram,  and D. Isa, "Using unsupervised clustering approach to train the Support Vector Machine for text classification", Neurocomputing, vol. 211, pp. 4-10, 2016.
[http://dx.doi.org/10.1016/j.neucom.2015.10.137] 
[19] 
Y. Rao, H. Xie, J. Li, F. Jin, F.L. Wang,  and Q. Li, "Social emotion classification of short text via topic-level maximum entropy model", Inf. Manage., vol. 53, pp. 978-986, 2016.
[http://dx.doi.org/10.1016/j.im.2016.04.005] 
[20] 
Z. Zhang, Q. He, J. Gao,  and M. Ni, "A deep learning approach for detecting traffic accidents from social media data", Transp. Res., Part C Emerg. Technol., vol. 86, pp. 580-596, 2018.
[http://dx.doi.org/10.1016/j.trc.2017.11.027] 
[21] 
F. Ali, D. Kwak, P. Khan, S.M.R. Islam, K.H. Kim,  and K.S. Kwak, "Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling", Transp. Res., Part C Emerg. Technol., vol. 77, pp. 33-48, 2017.
[http://dx.doi.org/10.1016/j.trc.2017.01.014] 
[22] 
M.A. Saloot, N. Idris,  and R. Mahmud, "An architecture for Malay Tweet normalization", Inf. Process. Manage., vol. 50, pp. 621-633, 2014.
[http://dx.doi.org/10.1016/j.ipm.2014.04.009] 
[23] 
S. Vashishtha,  and S. Susan, "Fuzzy rule based unsupervised sentiment analysis from social media posts", Expert Syst. Appl, vol. 138. 2019. 112834
[http://dx.doi.org/10.1016/j.eswa.2019.112834] 
[24] 
J.W. Bi, Y. Liu,  and Z.P. Fan, "Representing sentiment analysis results of online reviews using interval type-2 fuzzy numbers and its application to product ranking", Inf. Sci. (Ny), vol. 504, pp. 293-307, 2019.
[http://dx.doi.org/10.1016/j.ins.2019.07.025] 
[25] 
F. Riquelme,  and P. González-Cantergiani, "Measuring user influence on Twitter: A survey", Inf. Process. Manage., vol. 52, pp. 949-975, 2016.
[http://dx.doi.org/10.1016/j.ipm.2016.04.003] 
[26] 
C. Lipizzi, D.G. Dessavre, L. Iandoli,  and J.E. Ramirez Marquez, "Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations", Comput. Human Behav., vol. 64, pp. 782-792, 2016.
[http://dx.doi.org/10.1016/j.chb.2016.07.030] 
[27] 
E.S. Tellez, S. Miranda-Jiménez, M. Graff, D. Moctezuma, R.R. Suárez,  and O.S. Siordia, "A simple approach to multilingual polarity classification in Twitter", Pattern Recognit. Lett., vol. 94, pp. 68-74, 2017.
[http://dx.doi.org/10.1016/j.patrec.2017.05.024] 
[28] 
S. Akyol,  and B. Alatas, "Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization", Phys. A Stat. Mech. its Appl, 2020.
[http://dx.doi.org/10.1016/j.physa.2019.123094] 
[29] 
A.C.E.S. Lima, L.N. De Castro,  and J.M. Corchado, "A polarity analysis framework for Twitter messages", Appl. Math. Comput., vol. 270, pp. 756-767, 2015.
[http://dx.doi.org/10.1016/j.amc.2015.08.059] 
[30] 
F.H. Khan, S. Bashir,  and U. Qamar, "TOM: Twitter opinion mining framework using hybrid classification scheme", Decis. Support Syst., vol. 57, pp. 245-257, 2014.
[http://dx.doi.org/10.1016/j.dss.2013.09.004] 
[31] 
S. Liu, X. Cheng, F.F. Li,  and F.F. Li, "TASC: Topic-adaptive sentiment classification on dynamic tweets", IEEE Trans. Knowl. Data Eng., vol. 27, pp. 1696-1709, 2015.
[http://dx.doi.org/10.1109/TKDE.2014.2382600] 
[32] 
M. Fernández-Gavilanes, T. Álvarez-López, J. Juncal-Martínez, E. Costa-Montenegro,  and F. Javier González-Castaño, "Unsupervised method for sentiment analysis in online texts", Expert Syst. Appl., vol. 58, pp. 57-75, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.03.031] 
[33] 
R.R. Shah, Y. Yu, A. Verma, S. Tang, A.D. Shaikh,  and R. Zimmermann, "Leveraging multimodal information for event summarization and concept-level sentiment analysis", Knowl. Base. Syst., vol. 108, pp. 102-109, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.05.022] 
[34] 
D. Tang, F. Wei, B. Qin, N. Yang, T. Liu,  and M. Zhou, "Sentiment Embeddings with Applications to Sentiment Analysis", IEEE Trans. Knowl. Data Eng., vol. 28, pp. 496-509, 2016.
[http://dx.doi.org/10.1109/TKDE.2015.2489653] 
[35] 
J.V. Lochter, R.F. Zanetti, D. Reller,  and T.A. Almeida, "Short text opinion detection using ensemble of classifiers and semantic indexing", Expert Syst. Appl., vol. 62, pp. 243-249, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.06.025] 
[36] 
H. Saif,  and M. Fernandez, "Contextual Semantics for Sentiment Analysis of Twitter Categories and Subject Descriptors", 
[37] 
B. Altınel,  and M.C. Ganiz, "A new hybrid semi-supervised algorithm for text classification with class-based semantics", Knowl. Base. Syst., vol. 108, pp. 50-64, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.06.021] 
[38] 
B. Tunç, "Semantics of object representation in machine learning", Pattern Recognit. Lett., vol. 64, pp. 30-36, 2015.
[http://dx.doi.org/10.1016/j.patrec.2015.03.016] 
[39] 
A. Felbermayr,  and A. Nanopoulos, "The Role of Emotions for the Perceived Usefulness in Online Customer Reviews", J. Interact. Market, vol. 36, pp. 60-76, 2016.
[http://dx.doi.org/10.1016/j.intmar.2016.05.004] 
[40] 
S. Chakraverty,  and M. Saraswat, "Review based emotion profiles for cross domain recommendation", Multimedia Tools Appl., vol. 76, pp. 25827-25850, 2017.
[http://dx.doi.org/10.1007/s11042-017-4767-x] 
[41] 
M.Z. Asghar, F.M. Kundi, S. Ahmad, A. Khan,  and F. Khan, "T-SAF: Twitter sentiment analysis framework using a hybrid classification scheme", Expert Syst., vol. 35, 2018.e12233
[http://dx.doi.org/10.1111/exsy.12233] 
[42] 
Y. Wang, Y. Rao, X. Zhan, H. Chen, M. Luo,  and J. Yin, "Sentiment and emotion classification over noisy labels", Knowl. Base. Syst., vol. 111, pp. 207-216, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.08.012] 
[43] 
E.L.L. Iglesias, A. Seara Vieira,  and L. Borrajo, "An HMM-based over-sampling technique to improve text classification", Expert Syst. Appl., vol. 40, pp. 7184-7192, 2013.
[http://dx.doi.org/10.1016/j.eswa.2013.07.036] 
[44] 
Z. Lin, X. Jin, X. Xu, Y. Wang, X. Cheng, W. Wang,  and D. Meng, "An unsupervised cross-lingual topic model framework for sentiment classification", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 24, pp. 432-444, 2016.
[http://dx.doi.org/10.1109/TASLP.2015.2512041] 
[45] 
H.J. Escalante, M.A. García-Limón, A. Morales-Reyes, M. Graff, M. Montes-y-Gómez, E.F. Morales,  and J. Martínez-Carranza, "Term-weighting learning via genetic programming for text classification", Knowl. Base. Syst., vol. 83, pp. 176-189, 2015.
[http://dx.doi.org/10.1016/j.knosys.2015.03.025] 
[46] 
Y. Rao, H. Xie, J. Li, F. Jin, F. Lee,  and Q. Li, "Information & Management Social emotion classi fi cation of short text via topic-level maximum entropy model", Inf. Manage., vol. 53, pp. 978-986, 2016.
[http://dx.doi.org/10.1016/j.im.2016.04.005] 
[47] 
O. Vechtomova, "Disambiguating context-dependent polarity of words: An information retrieval approach", Inf. Process. Manage., vol. 53, pp. 1062-1079, 2017.
[http://dx.doi.org/10.1016/j.ipm.2017.03.007] 
[48] 
Z. Xu, X. Luo, S. Zhang, X. Wei, L. Mei,  and C. Hu, "Mining temporal explicit and implicit semantic relations between entities using web search engines", Future Gener. Comput. Syst., vol. 37, pp. 468-477, 2014.
[http://dx.doi.org/10.1016/j.future.2013.09.027] 
[49] 
M.Z. Asghar, S. Ahmad, M. Qasim, S.R. Zahra,  and F.M. Kundi, "SentiHealth: creating health-related sentiment lexicon using hybrid approach", Springerplus, vol. 5, no. 1, p. 1139, 2016.
[http://dx.doi.org/10.1186/s40064-016-2809-x] [PMID:  27504237] 
[50] 
K. Kim,  and O. Park, "joung, Yun, S., Yun, H.: What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management", Technol. Forecast. Soc. Change, vol. 123, pp. 362-369, 2017.
[http://dx.doi.org/10.1016/j.techfore.2017.01.001] 
[51] 
P. Wang, B. Xu, J. Xu, G. Tian, C-L.L. Liu,  and H. Hao, "Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification", Neurocomputing, vol. 174, pp. 806-814, 2016.
[http://dx.doi.org/10.1016/j.neucom.2015.09.096] 
[52] 
C. Hung,  and S-J.J. Chen, "Word sense disambiguation based sentiment lexicons for sentiment classification", Knowl. Base. Syst., vol. 110, pp. 224-232, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.07.030] 
[53] 
O. Appel, F. Chiclana, J. Carter,  and H. Fujita, "A hybrid approach to the sentiment analysis problem at the sentence level", Knowl. Base. Syst., vol. 108, pp. 110-124, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.05.040] 
[54] 
S. Poria, E. Cambria,  and A. Gelbukh, "Aspect Extraction for Opinion Miningwith a Deep Convolutional Neural Network", Knowl. Base. Syst., vol. 108, pp. 42-49, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.06.009] 
[55] 
M. Thelwall, "TensiStrength: Stress and relaxation magnitude detection for social media texts", Inf. Process. Manage., vol. 53, pp. 106-121, 2017.
[http://dx.doi.org/10.1016/j.ipm.2016.06.009] 
[56] 
H. Keshavarz,  and M.S. Abadeh, "ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs", Knowl. Base. Syst., vol. 122, pp. 1-16, 2017.
[http://dx.doi.org/10.1016/j.knosys.2017.01.028] 
[57] 
F.H. Khan, U. Qamar,  and S. Bashir, "eSAP: A decision support framework for enhanced sentiment analysis and polarity classification", Inf. Sci. (Ny), vol. 367–368, pp. 862-873, 2016.
[http://dx.doi.org/10.1016/j.ins.2016.07.028] 
[58] 
M.S. Rani,  and S. Sumathy, "Opinion mining on social media transit tweets using text pre-processing and machine learning techniques", Int. J. Innov. Technol. Explor. Eng., vol. 9, pp. 1015-1025, 2019.
[http://dx.doi.org/10.35940/ijitee.A4631.119119] 
[59] 
K. Buss, "Literature Review on Preprocessing for Text Mining", Inst. Creat. Technol, pp. 1-33, 2008.
[60] 
J. Fürnkranz, "A Study Using n\ngram Features for Text Categorization", Austrian Res. Inst. Artifical Intell, 1998.
[61] 
R. Parikh,  and M. Movassate, "Sentiment analysis of user-generated twitter updates using various classification techniques", 
[62] 
M. Oussalah, F. Bhat, K. Challis,  and T. Schnier, "A software architecture for Twitter collection, search and geolocation services", Knowl. Base. Syst., vol. 37, pp. 105-120, 2013.
[http://dx.doi.org/10.1016/j.knosys.2012.07.017] 
[63] 
E. Haddi, X. Liu,  and Y. Shi, "The Role of Text Pre-processing in Sentiment Analysis", Procedia Comput. Sci., vol. 17, pp. 26-32, 2013.
[http://dx.doi.org/10.1016/j.procs.2013.05.005] 
[64] 
R. Duwairi,  and M. El-Orfali, "A study of the effects of preprocessing strategies on sentiment analysis for Arabic text", J. Inf. Sci., vol. 40, pp. 501-513, 2014.
[http://dx.doi.org/10.1177/0165551514534143] 
[65] 
B. Ma, H. Yuan,  and Y. Wu, "Exploring performance of clustering methods on document sentiment analysis", J. Inf. Sci., vol. 43, pp. 54-74, 2017.
[http://dx.doi.org/10.1177/0165551515617374] 
[66] 
D. Effrosynidis, S. Symeonidis,  and A. Arampatzis, "A comparison of pre-processing techniques for twitter sentiment analysis", Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 2017, pp. 394-406.
[http://dx.doi.org/10.1007/978-3-319-67008-9_31] 
[67] 
Z. Jianqiang,  and G. Xiaolin, "Comparison research on text pre-processing methods on twitter sentiment analysis", IEEE Access, vol. 5, pp. 2870-2879, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2672677] 
[68] 
A. Tripathy, A. Agrawal,  and S.K. Rath, "Classification of sentiment reviews using n-gram machine learning approach", Expert Syst. Appl., vol. 57, pp. 117-126, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.03.028] 
[69] 
P.H.H. Shahana,  and B. Omman, "Evaluation of features on sentimental analysis", Procedia Comput. Sci., vol. 46, pp. 1585-1592, 2015.
[http://dx.doi.org/10.1016/j.procs.2015.02.088] 
[70] 
A.K. Uysal, "An improved global feature selection scheme for text classification", Expert Syst. Appl., vol. 43, pp. 82-92, 2016.
[http://dx.doi.org/10.1016/j.eswa.2015.08.050] 
[71] 
V.J. Nirmal,  and D.I.G. Amalarethinam, "Parallel Implementation of Big Data Pre-Processing Algorithms for Sentiment Analysis of Social Networking Data", Intern. J. Fuzzy Math. Arch., vol. 6, pp. 149-159, 2015.
[72] 
K. Ogada, W. Mwangi,  and C. Wilson, "N-gram Based Text Categorization Method for Improved Data Mining", J. Inf. Eng. Appl., vol. 5, pp. 35-44, 2015.
[73] 
D. Agnihotri, K. Verma,  and P. Tripathi, "Variable Global Feature Selection Scheme for automatic classification of text documents", Expert Syst. Appl., vol. 81, pp. 268-281, 2017.
[http://dx.doi.org/10.1016/j.eswa.2017.03.057] 
[74] 
J. Bi, S. Li, H. Yuan, Z. Zhao,  and H. Liu, "Deep neural networks for predicting task time series in cloud computing systems", Proc. 2019 IEEE 16th Int. Conf. Networking, Sens. Control. ICNSC, 2019.
[http://dx.doi.org/10.1109/ICNSC.2019.8743188] 
[75] 
J. Bi, H. Yuan,  and M. Zhou, "Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds", IEEE Trans. Autom. Sci. Eng., vol. 16, pp. 1763-1773, 2019.
[http://dx.doi.org/10.1109/TASE.2019.2895801] 
[76] 
C. Lu, L. Peng, J. Bi,  and H. Yuan, "An Improved Dro-Based Recurrent Neural Networks for Large-Scale Light Curve Time Series Prediction", Proc. 2018 5th IEEE Int. Conf. Cloud Comput. Intell. Syst. CCIS, 2019.
[77] 
L. Zhang,  and J. Bi, "H.Y.: Workload Forecasting with Hybrid Stochastic configuration Networks in Clouds", Proceedings of, vol. CCIS2018, pp. 112-116, 2018.
[78] 
E. Tan, "Spam Analysis and Detection for User Generated Content in Online Social Networks", 
[79] 
A. Omar, M. Bhutta,  and G. Lepeu, "Managing spam: a global challenge", Int. J. Manag. Pract., vol. 3, pp. 405-416, 2009.
[http://dx.doi.org/10.1504/IJMP.2009.026964] 
[80] 
M. Jiang, P. Cui,  and C. Faloutsos, "Suspicious Behavior Detection: Current Trends and Future Directions", IEEE Intell. Syst., vol. 31, pp. 31-39, 2016.
[http://dx.doi.org/10.1109/MIS.2016.5] 
[81] 
M.S. Rani,  and S. Sumathy, "Online social networking services and spam detection approaches in opinion mining – A review", Int. J. Web Based Commun., vol. 14, pp. 353-378, 2018.
[http://dx.doi.org/10.1504/IJWBC.2018.096245] 
[82] 
F. Wu, J. Shu, Y. Huang,  and Z. Yuan, "Co-detecting social spammers and spam messages in microblogging via exploiting social contexts", Neurocomputing, vol. 201, pp. 51-65, 2016.
[http://dx.doi.org/10.1016/j.neucom.2016.03.036] 
[83] 
S. Abu-Nimeh, T. Chen,  and O. Alzubi, "Malicious and spam posts in online social networks", Computer (Long. Beach. Calif), vol. 44, pp. 23-28, 2011.
[http://dx.doi.org/10.1109/MC.2011.222] 
[84] 
C. Trattner,  and F. Kappe, "Social stream marketing on Facebook: a case study", Int. J. Sociol., vol. 2, pp. 86-103, 2013.
[85] 
F. Ahmed,  and M. Abulaish, "A generic statistical approach for spam detection in Online Social Networks", Comput. Commun., vol. 36, pp. 1120-1129, 2013.
[http://dx.doi.org/10.1016/j.comcom.2013.04.004] 
[86] 
H. Agrawal,  and R. Kaushal, "Analysis of text mining techniques over public pages of Facebook", Adv. Comput., vol. 2016, pp. 9-14, 2016. [IACC].
[http://dx.doi.org/10.1109/IACC.2016.12] 
[87] 
K. Lee, B.D. Eoff,  and J. Caverlee, "Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter", Icwsm, vol. 2011, pp. 185-192, 2006.
[88] 
J. Martinez-Romo,  and L. Araujo, "Detecting malicious tweets in trending topics using a statistical analysis of language", Expert Syst. Appl., vol. 40, pp. 2992-3000, 2013.
[http://dx.doi.org/10.1016/j.eswa.2012.12.015] 
[89] 
S. Jeong, G. Noh, H. Oh,  and C. Kim, "Follow Spam Detection based on Cascaded Social Information", Inf. Sci. (Ny), vol. 369, pp. 481-499, 2016.
[http://dx.doi.org/10.1016/j.ins.2016.07.033] 
[90] 
C. Chen, S. Wen, J. Zhang, Y. Xiang,  and J. Oliver, "Investigating the deceptive information in Twitter spam", Futur. Gener., vol. 72, pp. 319-326, 2017.
[http://dx.doi.org/10.1016/j.future.2016.05.036] 
[91] 
G. Lin, N. Sun, S. Nepal, J. Zhang,  and Y. Xiang, "Statistical Twitter Spam Detection Demystified: Performance, Stability and Scalability", IEEE Access, vol. 5, pp. 11142-11154, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2710540] 
[92] 
M. Washha, A. Qaroush, M. Mezghani,  and F. Sedes, "Unsupervised collective-based framework for dynamic retraining of supervised real-time spam tweets detection model", Expert Syst. Appl., vol. 135, pp. 129-152, 2019.
[http://dx.doi.org/10.1016/j.eswa.2019.05.052] 
[93] 
Y. Lin, T. Zhu, X. Wang, J. Zhang,  and A. Zhou, "Towards online review spam detection", 23rd Int. Conf. World Wide Web, 2014pp. 341-342 
[94] 
B. Zhou,  and M. Baltimore, Online Review Spam Detection by New Linguistic Features.iConference 2015 Proceedings, 2015, pp. 1-5.
[95] 
H. Hong, D. Xu, G.A. Wang,  and W. Fan, "Understanding the determinants of online review helpfulness: A meta-analytic investigation", Decis. Support Syst., vol. 102, pp. 1-11, 2017.
[http://dx.doi.org/10.1016/j.dss.2017.06.007] 
[96] 
S.J. Delany, M. Buckley,  and D. Greene, "SMS spam filtering: Methods and data", Expert Syst. Appl., vol. 39, pp. 9899-9908, 2012.
[http://dx.doi.org/10.1016/j.eswa.2012.02.053] 
[97] 
A. Karami,  and L. Zhou, "Exploiting Latent Content based Features for the Detection of Static SMS Spams", Proceedings of the American Society for Information Science and Technology, 2014pp. 1-4 
[http://dx.doi.org/10.1002/meet.2014.14505101157] 
[98] 
A. Karami,  and L. Zhou, "Improving Static SMS Spam Detection by Using New Content-based Features", Americas Conference on Information Systems, 2014pp. 1-9 
[99] 
A. Al-Hasan,  and E. El-Alfy, Dendritic cell algorithm for mobile phone spam filtering.InANT/SEIT., 2015, pp. 244-251.
[http://dx.doi.org/10.1016/j.procs.2015.05.067] 
[100] 
M. Latiff, H. Chiroma, O. Osho,  and G. Abdul-Salaam, "A Review on Mobile SMS Spam Filtering Techniques", IEEE Access, vol. 5, pp. 15650-15666, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2666785] 
[101] 
D.M. Blei, B.B. Edu, A.Y. Ng, A.S. Edu, M.I. Jordan,  and J.B. Edu, "Latent Dirichlet Allocation", J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
[102] 
M. Chakraborty, S. Pal,  and R. Pramanik, "Recent developments in social spam detection and combating techniques: A survey", Inf. Process., vol. 52, pp. 1053-1073, 2016.
[http://dx.doi.org/10.1016/j.ipm.2016.04.009] 
[103] 
F. Vanhoenshoven, G. Nápoles,  and R. Falcon, "Detecting malicious URLs using machine learning techniques (SSCI), IEEE", 
[104] 
S. Kumar, J. Cheng,  and J. Leskovec, "Antisocial Behavior on the Web", Proc. 26th Int. Conf. World Wide Web Companion - WWW ’17 Companion, 2017.
[http://dx.doi.org/10.1145/3041021.3051106] 
[105] 
M. Azad,  and R. Morla, "Early identification of spammers through identity linking, social network and call features", J. Comput. Sci., vol. 23, pp. 157-172, 2016.
[http://dx.doi.org/10.1016/j.jocs.2016.10.019] 
[106] 
K. Adewole, N. Anuar, A. Kamsin,  and K. Varathan, "Malicious accounts: dark of the social networks", J. Netw. Comput. Appl., vol. 79, pp. 41-67, 2017.
[http://dx.doi.org/10.1016/j.jnca.2016.11.030] 
[107] 
R. Hong, C. He, Y. Ge,  and M. Wang, "User Vitality Ranking and Prediction in Social Networking Services: A Dynamic Network Perspective", IEEE Trans. Knowl. Data Eng., vol. 29, pp. 1343-1356, 2017.
[http://dx.doi.org/10.1109/TKDE.2017.2672749] 
[108] 
 Ala’M, "Paris, H.: Spam profile detection in social networks based on public features", 2017 8th Int. Conf. Inf. Commun. Syst., 2017, pp. 130-135.
[109] 
R. Dewang,  and A. Singh, "Identification of fake reviews using new set of lexical and syntactic features", Proc. Sixth Int. Conf. Comput. Commun. Technol., 2015pp. 115-119 
[110] 
M. Rajdev,  and K. Lee, "Fake and spam messages: Detecting misinformation during natural disasters on social media. Proc. - 2015 IEEE/WIC/ACM Int", Jt. Conf. Web Intell. Intell. Agent Technol. WI-IAT, 2015, 2016, pp. 17-20.
[111] 
S. Ahmed, R. Monzur,  and R. Palit, "Development of a Rumor and Spam Reporting and Removal Tool for Social Media", 2016 3rd Asia-Pacific World Congr. Comput. Sci. Eng. (APWC CSE), 2016, pp. 157-163.
[112] 
M. Viviani,  and G. Pasi, "Credibility in social media: opinions, news, and health information—a survey", Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 75, 2017.e1209
[http://dx.doi.org/10.1002/widm.1209] 
[113] 
S. Shehnepoor, M. Salehi, R. Farahbakhsh,  and N. Crespi, "NetSpam: A Network-Based Spam Detection Framework for Reviews in Online Social Media", IEEE Trans. Inf. Forensics Security, vol. 12, pp. 1585-1595, 2017.
[http://dx.doi.org/10.1109/TIFS.2017.2675361] 
[114] 
A. Heydari,  and M. Tavakoli, "ali, Salim, N., Heydari, Z.: Detection of review spam: A survey", Expert Syst. Appl., vol. 42, pp. 3634-3642, 2015.
[http://dx.doi.org/10.1016/j.eswa.2014.12.029] 
[115] 
A. Mukherjee, B. Liu,  and N. Glance, "Spotting fake reviewer groups in consumer reviews", Proceedings of the 21st international conference on World Wide Web, 2012, pp. 191-200.
[http://dx.doi.org/10.1145/2187836.2187863] 
[116] 
R.Y.K. Lau, S.Y. Liao, R. Chi-Wai Kwok, K. Xu, Y. Xia,  and Y. Li, "Text mining and probabilistic language modeling for online review spam detection", ACM Trans. Manag. Inf. Syst., vol. 2, pp. 1-30, 2011.
[http://dx.doi.org/10.1145/2070710.2070716] 
[117] 
N. Hu, I. Bose, N. Koh,  and L. Liu, "Manipulation of online reviews: An analysis of ratings, readability, and sentiments", Decis. Support Syst., vol. 52, pp. 674-684, 2012.
[http://dx.doi.org/10.1016/j.dss.2011.11.002] 
[118] 
A. Morales, H. Sun,  and X. Yan, "Synthetic review spamming and defense", 22nd Int. Conf. World Wide Web, 2013pp. 155-156 
[119] 
Y. Suhara, H. Toda, S. Nishioka,  and S. Susaki, "Automatically generated spam detection based on sentence-level topic information", Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 1157-1160.
[http://dx.doi.org/10.1145/2487788.2488140] 
[120] 
Y. Lin, T. Zhu, H. Wu, J. Zhang,  and X. Wang, "Towards online anti-opinion spam: Spotting fake reviews from the review sequence", 2014 IEEE/ACM Int. Conf. Adv. Soc. networks Anal. Min. (ASONAM 2014), 2014, pp. 261-264.
[121] 
D. Savage, X. Zhang, X. Yu, P. Chou,  and Q. Wang, "Detection of opinion spam based on anomalous rating deviation", Expert Syst., vol. 42, pp. 8650-8657, 2016.
[http://dx.doi.org/10.1016/j.eswa.2015.07.019] 
[122] 
T. Zhu, H. Gao, Y. Yang, K. Bu,  and Y. Chen, "Beating the artificial chaos: fighting OSN spam using its own templates", IEEE/ACM Trans. Netw., vol. 24, pp. 3856-3869, 2016.
[http://dx.doi.org/10.1109/TNET.2016.2557849] 
[123] 
Y. Ren,  and D. Ji, "Neural networks for deceptive opinion spam detection: An empirical study", Inf. Sci. (Ny), vol. 385, pp. 213-224, 2017.
[http://dx.doi.org/10.1016/j.ins.2017.01.015] 
[124] 
L. Li, B. Qin, W. Ren,  and T. Liu, "Document representation and feature combination for deceptive spam review detection", Neurocomputing, vol. 254, pp. 33-41, 2017.
[http://dx.doi.org/10.1016/j.neucom.2016.10.080] 
[125] 
C. Rădulescu,  and M. Dinsoreanu, "Identification of spam comments using natural language processing techniques", 2014 IEEE 10th Int. Conf. Intell. Comput. Commun. Process., 2014, pp. 29-35.
[126] 
M. Hazim, N.B. Anuar, M.F. Ab Razak,  and N.A. Abdullah, "Detecting opinion spams through supervised boosting approach", PLoS One, vol. 13, no. 6, 2018.e0198884
[http://dx.doi.org/10.1371/journal.pone.0198884] [PMID:  29889897] 
[127] 
S.P. Rajamohana,  and K. Umamaheswari, "Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection", Proceedings of the 9th International Conference on Machine Learning and Computing - ICMLC 2017, 2017, pp. 238-242.
[128] 
N. Jindal, B. Liu,  and E-P. Lim, "Finding unusual review patterns using unexpected rules", Proc. 19th ACM Int. Conf. Inf. Knowl. Manag. - CIKM ’10. 1549, 2010.
[http://dx.doi.org/10.1145/1871437.1871669] 
[129] 
A. Mukherjee, A. Kumar, B. Liu, J. Wang,  and M. Hsu, "Spotting opinion spammers using behavioral footprints", 19th ACM SIGKDD Int. Conf. Knowl. Discov. data Min., 2013, pp. 632-640.
[130] 
V. Sandulescu,  and M. Ester, "Detecting Singleton Review Spammers Using Semantic Similarity", 24th Int. Conf. World, 2016 
[131] 
A. Heydari, M. Tavakoli,  and N. Salim, "Detection of fake opinions using time series", Expert Syst. Appl., vol. 58, pp. 83-92, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.03.020] 
[132] 
KC S.,  and Mukherjee A., "On the temporal dynamics of opinion spamming: Case studies on yelp", Proceedings of the 25th International Conference on World Wide Web, 2016pp. 369-379 
[133] 
L. You, Q. Peng, Z. Xiong, D. He, M. Qiu,  and X. Zhang, "Integrating aspect analysis and local outlier factor for intelligent review spam detection", Future Gener. Comput. Syst., vol. 102, pp. 163-172, 2020.
[http://dx.doi.org/10.1016/j.future.2019.07.044] 
[134] 
K. Dhingra,  and S.K. Yadav, "Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop", Int. J. Mach. Learn. Cybern., vol. 10, pp. 2143-2162, 2019.
[http://dx.doi.org/10.1007/s13042-017-0768-3] 
[135] 
D. Fusilier, M. Montes-y-Gómez,  and P. Rosso, "Detecting positive and negative deceptive opinions using PU-learning", Inf. Process., vol. 51, pp. 433-443, 2015.
[http://dx.doi.org/10.1016/j.ipm.2014.11.001] 
[136] 
M. Ahsan, T. Nahian,  and A. Kafi, "Review spam detection using active learning", 2016 IEEE 7th Annu. Inf. Technol. Electron. Mob. Commun. Conf., 2016, pp. 1-7.
[137] 
J. Rout, A. Dalmia, K. Choo,  and S. Bakshi, "Revisiting Semi-Supervised Learning for Online Deceptive Review Detection", IEEE Access, vol. 5, pp. 1319-1327, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2655032] 
[138] 
S. Chatterjee, A. Mukhopadhyay,  and M. Bhattacharyya, "Dependent judgment analysis: A Markov chain based approach for aggregating crowdsourced opinions", Inf. Sci. (Ny), vol. 396, pp. 83-96, 2017.
[http://dx.doi.org/10.1016/j.ins.2017.01.036] 
[139] 
S. Kim, S. Lee, D. Park,  and J. Kang, "Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset", Proc. 26th Int. Conf. World Wide Web - WWW ’17, 2017, pp. 827-836.
[http://dx.doi.org/10.1145/3038912.3052607] 
[140] 
S. Algur, A. Patil,  and P. Hiremath, "Conceptual level similarity measure based review spam detection", 2010 Int. Conf. Signal Image Process, 2010, pp. 416-423.
[http://dx.doi.org/10.1109/ICSIP.2010.5697509] 
[141] 
A. Aggarwal, J. Almeida,  and P. Kumaraguru, "Detection of spam tipping behaviour on foursquare", Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 641-648.
[http://dx.doi.org/10.1145/2487788.2488015] 
[142] 
R. Yin, H. Wang,  and L. Liu, "Research of integrated algorithm establishment of a spam detection system", 2015 4th Int. Conf. Comput. Sci. Netw. Technol., vol. 1. 2015, pp. 584-589.
[143] 
A. Munzel, "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus", J. Retailing Consum. Serv., vol. 32, pp. 96-108, 2016.
[http://dx.doi.org/10.1016/j.jretconser.2016.06.002] 
[144] 
C. Xu, J. Zhang, K. Chang,  and C. Long, "Uncovering collusive spammers in Chinese review websites", Proc. 22nd ACM Int. Conf. Conf. Inf. Knowl. Manag. - CIKM ’13, 2013, pp. 979-988.
[http://dx.doi.org/10.1145/2505515.2505700] 
[145] 
Y. Lu, L. Zhang, Y. Xiao,  and Y. Li, "Simultaneously detecting fake reviews and review spammers using factor graph model", Proceedings of the 5th annual ACM web science conference, 2013, pp. 225-233.
[http://dx.doi.org/10.1145/2464464.2464470] 
[146] 
Z. Wang, S. Gu, X. Zhao,  and X. Xu, "Graph-based review spammer group detection", Knowl. Inf. Syst., vol. 55, pp. 571-597, 2018.
[http://dx.doi.org/10.1007/s10115-017-1068-7] 
[147] 
Y. Liu, B. Pang,  and X. Wang, "Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph", Neurocomputing, vol. 366, pp. 276-283, 2019.
[http://dx.doi.org/10.1016/j.neucom.2019.08.013] 
[148] 
S. Noekhah,  and N. Salim, "binti, Zakaria, N.H.: Opinion spam detection: Using multi-iterative graph-based model", Inf. Process. Manage, vol. 57. 2020. 102140
[http://dx.doi.org/10.1016/j.ipm.2019.102140] 
[149] 
M.S. Akhtar, S. Kohail, A. Kumar, A. Ekbal,  and C. Biemann, "Feature selection using multi-objective optimization for aspect based sentiment analysis", In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 10260 LNCS., 2017, pp. 15-27.
[http://dx.doi.org/10.1007/978-3-319-59569-6_2] 
[150] 
O. Alfarraj,  and A.A. AlZubi, "A novel approach for ranking customer reviews using a modified PSO-based aspect ranking algorithm", Cluster Comput., vol. 22, pp. 3175-3181, 2018.
[http://dx.doi.org/10.1007/s10586-018-2012-7] 
[151] 
M. Al-Smadi, M. Al-Ayyoub, Y. Jararweh,  and O. Qawasmeh, "Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological, syntactic and semantic features", Inf. Process. Manage., vol. 56, pp. 308-319, 2019.
[http://dx.doi.org/10.1016/j.ipm.2018.01.006] 
[152] 
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. Comput. Sci., vol. 27, pp. 386-393, 2018.
[http://dx.doi.org/10.1016/j.jocs.2017.11.006] 
[153] 
M.H. Alam, W-J. Ryu,  and S. Lee, "Joint multi-grain topic sentiment: modeling semantic aspects for online reviews", Inf. Sci. (Ny), vol. 339, pp. 206-223, 2016.
[http://dx.doi.org/10.1016/j.ins.2016.01.013] 
[154] 
T. Álvarez-López, M. Fernández-Gavilanes, E. Costa-Montenegro,  and P. Bellot, "A proposal for book oriented aspect based sentiment analysis: Comparison over domains", In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 2018, pp. 3-14.
[155] 
R.K. Amplayo, S. Lee,  and M. Song, "Incorporating product description to sentiment topic models for improved aspect-based sentiment analysis", Inf. Sci. (Ny), vol. 454–455, pp. 200-215, 2018.
[http://dx.doi.org/10.1016/j.ins.2018.04.079] 
[156] 
M.Z. Asghar, A. Khan, S.R. Zahra, S. Ahmad,  and F.M. Kundi, "Aspect-based opinion mining framework using heuristic patterns", Cluster Comput., vol. 22, pp. 1-19, 2017.
[157] 
A. Bandhakavi, N. Wiratunga, D. Padmanabhan,  and S. Massie, "Lexicon based feature extraction for emotion text classification", Pattern Recognit. Lett., vol. 93, pp. 133-142, 2017.
[http://dx.doi.org/10.1016/j.patrec.2016.12.009] 
[158] 
D. Barman,  and N. Chowdhury, A nonnegative matrix factorization based approach to extract aspects from product reviews., Advances in Intelligent Systems and Computing, 2018, pp. 255-266.
[http://dx.doi.org/10.1007/978-981-10-8237-5_25] 
[159] 
S. Aiyar,  and N.P. Shetty, "N-Gram Assisted Youtube Spam Comment Detection", Procedia Comput. Sci., vol. 132, pp. 174-182, 2018.
[http://dx.doi.org/10.1016/j.procs.2018.05.181] 
[160] 
C. Wu, F. Wu, S. Wu, Z. Yuan,  and Y. Huang, "A hybrid unsupervised method for aspect term and opinion target extraction", Knowl. Base. Syst., vol. 148, pp. 66-73, 2018.
[http://dx.doi.org/10.1016/j.knosys.2018.01.019] 
[161] 
A. Chandra Pandey, D. Singh Rajpoot,  and M. Saraswat, "Twitter sentiment analysis using hybrid cuckoo search method", Inf. Process. Manage., vol. 53, pp. 764-779, 2017.
[http://dx.doi.org/10.1016/j.ipm.2017.02.004] 
[162] 
M. Dragoni, C. Da Costa Pereira, A.G.B. Tettamanzi,  and S. Villata, "Combining argumentation and aspect-based opinion mining: The SMACk system", AI Commun., vol. 31, pp. 75-95, 2018.
[http://dx.doi.org/10.3233/AIC-180752] 
[163] 
M. Dragoni, M. Federici,  and A. Rexha, "An unsupervised aspect extraction strategy for monitoring real-time reviews stream", Inf. Process. Manage., vol. 56, pp. 1103-1118, 2018.
[http://dx.doi.org/10.1016/j.ipm.2018.04.010] 
[164] 
F. Chen, Z. Yuan,  and Y. Huang, "Multi-source data fusion for aspect-level sentiment classification", Knowl. Base. Syst, vol. 187. 2019. 104831
[http://dx.doi.org/10.1016/j.knosys.2019.07.002] 
[165] 
A. García-Pablos, M. Cuadros,  and G. Rigau, "W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis", Expert Syst. Appl., vol. 91, pp. 127-137, 2018.
[http://dx.doi.org/10.1016/j.eswa.2017.08.049] 
[166] 
N. Gobi,  and A. Rathinavelu, "Analyzing cloud based reviews for product ranking using feature based clustering algorithm", Cluster Comput., vol. 22, pp. 6977-6984, 2018.
[http://dx.doi.org/10.1007/s10586-018-1996-3] 
[167] 
H. Jiang, C.K. Kwong, W.Y. Park,  and K.M. Yu, "A multi-objective PSO approach of mining association rules for affective design based on online customer reviews", J. Eng. Des., vol. 29, pp. 381-403, 2018.
[http://dx.doi.org/10.1080/09544828.2018.1475629] 
[168] 
"J, A.K., Abirami, S.: Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method", Inf. Sci. (Ny), vol. 460–461, pp. 23-41, 2018.
[169] 
P. Kalarani,  and S. Selva Brunda, "Sentiment analysis by POS and joint sentiment topic features using SVM and ANN", Soft Comput., vol. 23, pp. 7067-7079, 2018.
[http://dx.doi.org/10.1007/s00500-018-3349-9] 
[170] 
A. Konjengbam, N. Dewangan, N. Kumar,  and M. Singh, "Aspect ontology based review exploration", Electron. Commerce Res. Appl., vol. 30, pp. 62-71, 2018.
[http://dx.doi.org/10.1016/j.elerap.2018.05.006] 
[171] 
A. Laddha,  and A. Mukherjee, "Aspect opinion expression and rating prediction via LDA-CRF hybrid", Nat. Lang. Eng., vol. 24, pp. 611-639, 2018.
[http://dx.doi.org/10.1017/S135132491800013X] 
[172] 
R.Y.K. Lau, C. Li,  and S.S.Y. Liao, "Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis", Decis. Support Syst., vol. 65, pp. 80-94, 2014.
[http://dx.doi.org/10.1016/j.dss.2014.05.005] 
[173] 
M. Yang, Q. Jiang, Y. Shen, Q. Wu, Z. Zhao,  and W. Zhou, "Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning", Neural Netw., vol. 117, pp. 240-248, 2019.
[http://dx.doi.org/10.1016/j.neunet.2019.05.021] [PMID:  31195206] 
[174] 
E. Marrese-Taylor, J.D. Velásquez,  and F. Bravo-Marquez, "A novel deterministic approach for aspect-based opinion mining in tourism products reviews", Expert Syst. Appl., vol. 41, pp. 7764-7775, 2014.
[http://dx.doi.org/10.1016/j.eswa.2014.05.045] 
[175] 
Y.R. Nithin,  and G. Poornalatha, Feature based opinion mining for restaurant reviews., Advances in Intelligent Systems and Computing, 2018, pp. 305-318.
[176] 
Q. Liu, Z. Gao, B. Liu,  and Y. Zhang, "Automated rule selection for opinion target extraction", Knowl. Base. Syst., vol. 104, pp. 74-88, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.04.010] 
[177] 
L. Qiu, S. Gao, W. Cheng,  and J. Guo, "Aspect-based latent factor model by integrating ratings and reviews for recommender system", Knowl. Base. Syst., vol. 110, pp. 233-243, 2016.
[http://dx.doi.org/10.1016/j.knosys.2016.07.033] 
[178] 
T.A. Rana,  and Y.N. Cheah, "A two-fold rule-based model for aspect extraction", Expert Syst. Appl., vol. 89, pp. 273-285, 2017.
[http://dx.doi.org/10.1016/j.eswa.2017.07.047] 
[179] 
M. Rathan, V.R. Hulipalled, K.R. Venugopal,  and L.M. Patnaik, "Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews", Appl. Soft Comput. J., vol. 68, pp. 765-773, 2018.
[http://dx.doi.org/10.1016/j.asoc.2017.07.056] 
[180] 
S. Li, Z. Ming, Y. Leng,  and J. Guo, "Product ranking using hierarchical aspect structures", J. Intell. Inf. Syst., vol. 48, pp. 453-474, 2017.
[http://dx.doi.org/10.1007/s10844-016-0421-8] 
[181] 
S. Wu, F. Wu, Y. Chang, C. Wu,  and Y. Huang, "Automatic construction of target-specific sentiment lexicon", Expert Syst. Appl., vol. 116, pp. 285-298, 2019.
[http://dx.doi.org/10.1016/j.eswa.2018.09.024] 
[182] 
K. Schouten,  and F. Frasincar, "Survey on Aspect-Level Sentiment Analysis", IEEE Trans. Knowl. Data Eng., vol. 28, pp. 813-830, 2016.
[http://dx.doi.org/10.1109/TKDE.2015.2485209] 
[183] 
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 Trans. Cybern., vol. 48, no. 4, pp. 1263-1275, 2018.
[http://dx.doi.org/10.1109/TCYB.2017.2688801] [PMID:  28422676] 
[184] 
E. Souza, D. Santos, G. Oliveira, A. Silva,  and A.L.I. Oliveira, "Swarm optimization clustering methods for opinion mining", Nat. Comput., pp. 1-29, 2018.
[http://dx.doi.org/10.1007/s11047-018-9681-2] 
[185] 
A.D. Vo, Q.P. Nguyen,  and C.Y. Ock, "Opinion-Aspect Relations in Cognizing Customer Feelings via Reviews", IEEE Access, vol. 6, pp. 5415-5426, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2797224] 
[186] 
W.M. Wang, Z. Li, Z.G. Tian, J.W. Wang,  and M.N. Cheng, "Extracting and summarizing affective features and responses from online product descriptions and reviews: A Kansei text mining approach", Eng. Appl. Artif. Intell., vol. 73, pp. 149-162, 2018.
[http://dx.doi.org/10.1016/j.engappai.2018.05.005] 
[187] 
N. Zainuddin, A. Selamat,  and R. Ibrahim, "Hybrid sentiment classification on twitter aspect-based sentiment analysis", Appl. Intell., vol. 48, pp. 1218-1232, 2017.
[http://dx.doi.org/10.1007/s10489-017-1098-6] 
[188] 
Y. Zuo, J. Wu, H. Zhang, D. Wang,  and K. Xu, "Complementary Aspect-Based Opinion Mining", IEEE Trans. Knowl. Data Eng., vol. 30, pp. 249-262, 2018.
[http://dx.doi.org/10.1109/TKDE.2017.2764084] 
[189] 
F. Abid, M. Alam, M. Yasir,  and C. Li, "Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter", Future Gener. Comput. Syst., vol. 95, pp. 292-308, 2019.
[http://dx.doi.org/10.1016/j.future.2018.12.018] 
[190] 
F. Ali, D. Kwak, P. Khan, S. El-Sappagh, A. Ali, S. Ullah, K.H. Kim,  and K-S. Kwak, "Transportation sentiment analysis using word embedding and ontology-based topic modeling", Knowl. Base. Syst., vol. 174, pp. 27-42, 2019.
[http://dx.doi.org/10.1016/j.knosys.2019.02.033] 
[191] 
A. Kumar, K. Srinivasan, W.H. Cheng,  and A.Y. Zomaya, "Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data", Inf. Process. Manage., vol. 57, no. 102141, 2020.
[http://dx.doi.org/10.1016/j.ipm.2019.102141] 
[192] 
R. Bartusiak, Ł. Augustyniak, T. Kajdanowicz, P. Kazienko,  and M. Piasecki, "WordNet2Vec: Corpora agnostic word vectorization method", Neurocomputing, vol. 326–327, pp. 141-150, 2019.
[http://dx.doi.org/10.1016/j.neucom.2017.01.121] 
[193] 
B. Hu, B. Tang, Q. Chen,  and L. Kang, "A novel word embedding learning model using the dissociation between nouns and verbs", Neurocomputing, vol. 171, pp. 1108-1117, 2016.
[http://dx.doi.org/10.1016/j.neucom.2015.07.046] 
[194] 
A. Bagheri, M. Saraee,  and F. De Jong, An Unsupervised Aspect Detection Model for Sentiment Analysis of ReviewsInternational conference on application of natural language to information systems, 2013, pp. 140-151.
[http://dx.doi.org/10.1007/978-3-642-38824-8_12] 
[195] 
E.S. Chifu, T.S. Letia,  and V.R. Chifu, "Unsupervised Aspect Level Sentiment Analysis Using Self-Organizing Maps", 17th Int. Symp. Symb. Numer. Algorithms Sci. Comput. SYNASC 2015, 2016, pp. 468-475.
[196] 
D. Hyun, C. Park, M.C. Yang, I. Song, J.T. Lee,  and H. Yu, "Target-aware convolutional neural network for target-level sentiment analysis", Inf. Sci. (Ny), vol. 491, pp. 166-178, 2019.
[http://dx.doi.org/10.1016/j.ins.2019.03.076] 
[197] 
H.H. Do, P.W.C. Prasad, A. Maag,  and A. Alsadoon, "Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review", Expert Syst. Appl., vol. 118, pp. 272-299, 2019.
[http://dx.doi.org/10.1016/j.eswa.2018.10.003] 
[198] 
X. Fu, Y. Wei, F. Xu, T. Wang, Y. Lu, J. Li,  and J.Z. Huang, "Semi-supervised Aspect-level Sentiment Classification Model based on Variational Autoencoder", Knowl. Base. Syst., vol. 171, pp. 81-92, 2019.
[http://dx.doi.org/10.1016/j.knosys.2019.02.008] 
[199] 
X. Gu, Y. Gu,  and H. Wu, "Cascaded Convolutional Neural Networks for Aspect-Based Opinion Summary", Neural Process. Lett., vol. 46, pp. 581-594, 2017.
[http://dx.doi.org/10.1007/s11063-017-9605-7] 
[200] 
A. Ilmania, "Abdurrahman, Cahyawijaya, S., Purwarianti, A.: Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis", Proc. 2018 Int. Conf. Asian Lang. Process. IALP 2018, 2019, pp. 62-67.
[201] 
J. Chen, Y. Tao,  and H. Lin, "Visual exploration and comparison of word embeddings", J. Vis. Lang. Comput., vol. 48, pp. 178-186, 2018.
[http://dx.doi.org/10.1016/j.jvlc.2018.08.008] 
[202] 
N. Jihan, Y. Senarath,  and S. Ranathunga, "Aspect extraction from customer reviews using convolutional neural networks", 18th Int. Conf. Adv. ICT Emerg. Reg. ICTer 2018 - Proc., 2019, pp. 215-220.
[203] 
M. Kraus,  and S. Feuerriegel, "Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees", Expert Syst. Appl., vol. 118, pp. 65-79, 2019.
[http://dx.doi.org/10.1016/j.eswa.2018.10.002] 
[204] 
B.D. Nguyen-Hoang, Q.V. Ha,  and M.Q. Nghiem, Aspect-based sentiment analysis using word embedding restricted boltzmann machines. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., Springer: Cham, 2016, pp. 285-297.
[http://dx.doi.org/10.1007/978-3-319-42345-6_25] 
[205] 
M. Yang, Q. Qu, X. Chen, C. Guo, Y. Shen,  and K. Lei, "Feature-enhanced attention network for target-dependent sentiment classification", Neurocomputing, vol. 307, pp. 91-97, 2018.
[http://dx.doi.org/10.1016/j.neucom.2018.04.042] 
[206] 
N. Liu, B. Shen, Z. Zhang, Z. Zhang,  and K. Mi, "Attention-based Sentiment Reasoner for aspect-based sentiment analysis. Human-centric Comput", Inf. Sci., vol. 9, p. 35, 2019.
[207] 
H.T. Nguyen,  and M. Le Nguyen, "An ensemble method with sentiment features and clustering support", Neurocomputing, vol. 370, pp. 155-165, 2019.
[http://dx.doi.org/10.1016/j.neucom.2019.08.071] 
[208] 
D.H. Pham, A.C. Le,  and T.K.C. Le, "Learning Word Embeddings for Aspect-Based Sentiment Analysis", Commun. Comput. Inf. Sci., vol. 781, pp. 28-40, 2018.
[http://dx.doi.org/10.1007/978-981-10-8438-6_3] 
[209] 
D.H. Pham,  and A.C. Le, "Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis", Int. J. Approx. Reason., vol. 103, pp. 1-10, 2018.
[http://dx.doi.org/10.1016/j.ijar.2018.08.003] 
[210] 
D.H. Pham, T.T.T. Nguyen,  and A.C. Le, "Fine-tuning word embeddings for aspect-based sentiment analysis", International Conference on Text, Speech, and Dialogue, 2017, pp. 500-508.
[http://dx.doi.org/10.1007/978-3-319-64206-2_56] 
[211] 
D.H. Pham,  and A.C. Le, "Learning multiple layers of knowledge representation for aspect based sentiment analysis", Data Knowl. Eng., vol. 114, pp. 26-39, 2018.
[http://dx.doi.org/10.1016/j.datak.2017.06.001] 
[212] 
G. Pergola, L. Gui,  and Y. He, "TDAM: A topic-dependent attention model for sentiment analysis", Inf. Process. Manage, vol. 56. 2019, p. 102084.
[http://dx.doi.org/10.1016/j.ipm.2019.102084] 
[213] 
H. Park, "jung, Song, M., Shin, K.S.: Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories", Knowl. Base. Syst, vol. 187. 2020, p. 104825.
[http://dx.doi.org/10.1016/j.knosys.2019.06.033] 
[214] 
K. Pasupa,  and T. Seneewong Na Ayutthaya, "Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features", Sustain. Cities Soc., vol. 50. 2019, p. 101615.
[http://dx.doi.org/10.1016/j.scs.2019.101615] 
[215] 
P. Ray,  and A. Chakrabarti, "A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis", Appl. Comput. Informatics, 2019.
[216] 
R. Ma, K. Wang, T. Qiu, A.K. Sangaiah, D. Lin,  and H. Liaqat, "Bin: Feature-based Compositing Memory Networks for Aspect-based Sentiment Classification in Social Internet of Things", Future Gener. Comput. Syst., vol. 92, pp. 879-888, 2019.
[http://dx.doi.org/10.1016/j.future.2017.11.036] 
[217] 
S.M. Rezaeinia, R. Rahmani, A. Ghodsi,  and H. Veisi, "Sentiment analysis based on improved pre-trained word embeddings", Expert Syst. Appl., vol. 117, pp. 139-147, 2019.
[http://dx.doi.org/10.1016/j.eswa.2018.08.044] 
[218] 
M. Song, H. Park,  and K. Shin, "shik: Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean", Inf. Process. Manage., vol. 56, pp. 637-653, 2019.
[http://dx.doi.org/10.1016/j.ipm.2018.12.005] 
[219] 
R.A. Stein, P.A. Jaques,  and J.F. Valiati, "An analysis of hierarchical text classification using word embeddings", Inf. Sci. (Ny), vol. 471, pp. 216-232, 2019.
[http://dx.doi.org/10.1016/j.ins.2018.09.001] 
[220] 
K. Shuang, Z. Zhang, J. Loo,  and S. Su, "Convolution – deconvolution word embedding : An end-to-end multi-prototype fusion embedding method for natural language processing", Inf. Fusion, vol. 53, pp. 112-122, 2020.
[http://dx.doi.org/10.1016/j.inffus.2019.06.009] 
[221] 
S. Wu, Y. Xu, F. Wu, Z. Yuan, Y. Huang,  and X. Li, "Aspect-based sentiment analysis via fusing multiple sources of textual knowledge", Knowl. Base. Syst, vol. 183. 2019, p. 104868.
[http://dx.doi.org/10.1016/j.knosys.2019.104868] 
[222] 
W. Wang, S.J. Pan,  and D. Dahlmeier, "Memory networks for fine-grained opinion mining", Artif. Intell., vol. 265, pp. 1-17, 2018.
[http://dx.doi.org/10.1016/j.artint.2018.09.002] 
[223] 
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 Gener. Comput. Syst., vol. 93, pp. 304-311, 2019.
[http://dx.doi.org/10.1016/j.future.2018.10.041] 
[224] 
S. Xiong,  and D. Ji, "Exploiting flexible-constrained K-means clustering with word embedding for aspect-phrase grouping", Inf. Sci. (Ny), vol. 367–368, pp. 689-699, 2016.
[http://dx.doi.org/10.1016/j.ins.2016.07.002] 
[225] 
S. Xiong, H. Lv, W. Zhao,  and D. Ji, "Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings", Neurocomputing, vol. 275, pp. 2459-2466, 2018.
[http://dx.doi.org/10.1016/j.neucom.2017.11.023] 
[226] 
Y. Ma, H. Peng,  and E. Cambria, Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM.Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM., 2018, pp. 5876-5883.
[227] 
M.S. Rani,  and S. Subramanian, "Attention Mechanism with Gated Recurrent Unit Using Convolutional Neural Network for Aspect Level Opinion Mining", Arab. J. Sci. Eng., 2020.
[http://dx.doi.org/10.1007/s13369-020-04497-4] 
Cite As
Recent Patents on Engineering

A Study on Diverse Methods and Performance Measures in Sentiment Analysis

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Graphical Abstract
