[1]
B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions.Cambridge: Cambridge University Press, . 2015
[2]
A.C. Pandey, D.S. Rajpoot, and M. Saraswat, "Twitter sentiment analysis using hybrid cuckoo search method", Inform Proces. Manage., vol. 53, no. 4, pp. 764-779, . 2017
[3]
P.N. Howard, "The arab springs cascading effects" Pacific Standard 23. 2011
[4]
A. Pak, and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining", In: LREC, vol. 10. pp. 1320-1326. 2010
[5]
K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N.A. Smith, "Part-of-speech tagging for twitter: Annotation, features, and experiments", In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short papers-Volume 2, Association for Computational Linguistics, pp. 42-47. 2011
[6]
J.G. Shanahan, Y. Qu, and J. Wiebe, Computing attitude and affect in text: Theory and applications, Springer, vol. 20. , . 2006
[7]
R. Collobert, "J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa, “Natural language processing (almost) from scratch", J. Machine Learning Res., vol. 12, pp. 2493-2537, . 2011
[8]
T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean, " “Distributed representations of words and phrases and their compositionality"", In: Advances in Neural Information Processing Systems, pp. 3111-3119. 2013
[9]
K. Ravi, and V. Ravi, "A survey on opinion mining and sentiment analysis: Tasks, approaches and applications", Knowledge Based Syst., vol. 89, pp. 14-46, . 2015
[10]
A. Severyn, and A. Moschitti, "Twitter sentiment analysis with deep convolutional neural networks", In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp. 959-962. 2015
[11]
E. Riloff, and J. Wiebe, "Learning extraction patterns for subjective expressions", In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 105-112. 2003
[12]
S. Rill, D. Reinel, J. Scheidt, and R.V. Zicari, "Politwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis", Knowledge Based Syst., vol. 69, pp. 24-33, . 2014
[13]
O. Appel, F. Chiclana, J. Carter, and H. Fujita, "A hybrid approach to the sentiment analysis problem at the sentence level", Knowledge Based Syst., vol. 108, pp. 110-124, . 2016
[14]
A. Muhammad, N. Wiratunga, and R. Lothian, "Contextual sentiment analysis for social media genres", Knowledge Based Syst., vol. 108, pp. 92-101, . 2016
[15]
M. Fern’andez-Gavilanes, T. A’lvarez-L’opez, J. Juncal-Mart’ınez, E. Costa-Montenegro, and F.J. Gonz’alez-Castan˜o, "Unsupervised method for sentiment analysis in online texts", Expert Syst. Appl., vol. 58, pp. 57-75, . 2016
[16]
L. Jia, C. Yu, and W. Meng, "The effect of negation on sentiment analysis and retrieval effectiveness", In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, ACM, pp. 1827-1830. 2009
[17]
R. Narayanan, B. Liu, and A. Choudhary, "Sentiment analysis of conditional sentences", In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, vol. 1. pp. 180-189. 2009
[18]
O. Tsur, D. Davidov, and A. Rappoport, "Icwsm-a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews", In: Proceedings of 4th International AAAI Conference on Weblogs and Social Media, pp. 162-169. 2010
[19]
E. Riloff, A. Qadir, P. Surve, L. De Silva, N. Gilbert, and R. Huang, "Sarcasm as contrast between a positive sentiment and negative situation", In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 704-714. 2013
[20]
R. Gonz’alez-Ib’anez, S. Muresan, and N. Wacholder, "Identifying sarcasm in twitter: A closer look", In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, Association for Computational Linguistics, pp. 581-586. 2011
[21]
G.K. Pitsilis, H. Ramampiaro, and H. Langseth, "Effective hate-speech detection in twitter data using recurrent neural networks", Appl. Intell., pp. 1-13, . 2018
[22]
M. Mitchell, J. Aguilar, T. Wilson, and B. Van Durme, "Open domain targeted sentiment", In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1643-1654. 2013
[23]
M. Zhang, Y. Zhang, and D.T. Vo, "Neural networks for open domain targeted sentiment", In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 612-621. 2015
[24]
M. Hu, and B. Liu, "Mining and summarizing customer reviews", In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 168-177. 2004
[25]
V. Stoyanov, and C. Cardie, "Topic identification for fine-grained opinion analysis", In: Proceedings of the 22nd International Conference on Computational Linguistics, Association for Computational Linguistics, vol. 1. pp. 817-824. 2008
[26]
K. Liu, L. Xu, and J. Zhao, "Extracting opinion targets and opinion words from online reviews with graph co-ranking", In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1. pp. 314-324. 2014
[27]
D. Xue, L. Wu, Z. Hong, S. Guo, L. Gao, Z. Wu, X. Zhong, and J. Sun, "Deep learning-based personality recognition from text posts of online social networks", Appl. Intell., pp. 1-15, . 2018
[28]
R. Socher, J. Pennington, E.H. Huang, A.Y. Ng, and C.D. Manning, "Semi-supervised recursive auto-encoders for predicting sentiment distributions", In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 151-161. 2011
[29]
R. Socher, A. Perelygin, J. Wu, J. Chuang, C.D. Manning, A. Ng, and C. Potts, "Recursive deep models for semantic compositionality over a sentiment treebank", In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631-1642. 2013
[30]
N. Kalchbrenner, E. Grefenstette, and P. Blunsom, A convolutional neural network for modelling sentences, arXiv preprint arXiv: 14042188. 2014
[31]
Y. Kim, Convolutional neural networks for sentence classification, arXiv preprint arXiv: 14085882. 2014
[32]
K.S. Tai, R. Socher, and C.D. Manning, Improved semantic representations from tree-structured long short-term memory networks, arXiv preprint arXiv: 150300075. 2015
[33]
D. Tang, B. Qin, F. Wei, L. Dong, T. Liu, and M. Zhou, "A joint segmentation and classification framework for sentence level sentiment classification", IEEE/ACM Trans. Audio Speech Lang. Process., vol. 23, no. 11, pp. 1750-1761, . 2015
[34]
S. Poria, E. Cambria, and A. Gelbukh, "Aspect extraction for opinion mining with a deep convolutional neural network", Knowledge Based Syst., vol. 108, pp. 42-49, . 2016
[35]
P. Liu, X. Qiu, and X. Huang, Recurrent neural network for text classification with multi-task learning”, arXiv preprint arXiv: 160505101. 2016
[36]
I. Chaturvedi, Y.S. Ong, I.W. Tsang, R.E. Welsch, and E. Cambria, "Learning word dependencies in text by means of a deep recurrent belief network", Knowledge Based Syst., vol. 108, pp. 144-154, . 2016
[37]
N. Zainuddin, A. Selamat, and R. Ibrahim, "Hybrid sentiment classification on twitter aspect-based sentiment analysis", Appl. Intell., pp. 1-15, . 2018
[38]
T. Chen, R. Xu, Y. He, and X. Wang, "Improving sentiment analysis via sentence type classification using bilstm-crf and cnn", Expert Syst. Appl., vol. 72, pp. 221-230, . 2017
[39]
G. Preethi, P. V. Krishna, M. S. Obaidat, V. Saritha, and S. Yenduri S, “Application of deep learning to sentiment analysis for recommender system on cloud”, In IEEE 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 93-97. 2017
[40]
D. S. Sachan, M. Zaheer M, and R. Salakhutdinov R, “Revisiting lstm networks for semi-supervised text classification via mixed objective function”, KDD18 Deep Learning. 2018
[41]
S. Sohangir, D. Wang, A. Pomeranets, and T.M. Khoshgoftaar, "Big data: Deep learning for financial sentiment analysis", J. Big Data, vol. 5, no. 1, . 2018
[42]
Y. Yao, and Z. Huang, "Bi-directional LSTM recurrent neural network for Chinese word segmentation", In: International Conference on Neural Information Processing, Springer, pp. 345-353. 2016
[43]
F.A. Gers, and E. Schmidhuber, "LSTM recurrent networks learn simple context-free and context-sensitive languages", IEEE Trans. Neural Netw., vol. 12, no. 6, pp. 1333-1340, . 2001
[44]
T. Thireou, and M. Reczko, "Bidirectional long short-term memory networks for predicting the subcellular localization of eukaryotic proteins", IEEE/ACM Trans. Comput. Biol. Bioinform, vol. 4, no. 3, pp. 441-446, . 2007
[45]
M.C. Munteanu, A. Caliman, and C. Zaharia, “Convolutional Neural Network”, U. S. Patent 9,665,799. 2017
[46]
B. Graham, Fractional max-pooling, arXiv preprint arXiv: 14126071. 2014
[47]
S. Rosenthal, "Semeval dataset. URL", http://alt.qcri.org/ semeval2014/task9/
[48]
"(2015) Testdata.manual.2009.06.14. URL", http://help.sentiment140.com/for-students/
[49]
Sanders N., (2015) Twitter-sanders-apple, "URL", http://www.sananalytics.com/lab/twitter-sentiment/
[50]
A. Go, R. Bhayani, and L. Huang, Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford,, vol. 1, p. 12. 2009
[51]
H. Saif, M. Fernandez, Y. He, and Alani H, “Evaluation datasets for twitter sentiment analysis: A survey and a new dataset, the STSgold. 2013
[52]
S. Wang, and C.D. Manning, "Baselines and bigrams: Simple, good sentiment and topic classification", In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, vol. 2. pp. 90-94. 2012
[53]
O. Irsoy, and C. Cardie, "Deep recursive neural networks for compositionality in language", In: Advances in Neural Information Processing Systems, pp. 2096-2104. 2014
[54]
S. Sun, and Z. Xie, "Bilstm-based models for metaphor detection", In: National CCF Conference on Natural Language Processing and Chinese Computing, Springer, pp. 431-442. 2017
[55]
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. Machine Learn. Cybernet., pp. 1-13. 2018
[56]
J. Wang, L.C. Yu, K.R. Lai, and X. Zhang, "Dimensional sentiment analysis using a regional CNN-LSTM model", In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2. pp. 225-230. 2016
[57]
L. Guo, D. Zhang, L. Wang, H. Wang, and B. Cui, "Cran: A hybrid CNN-RNN attention-based model for text classification", In: International Conference on Conceptual Modeling, Springer, pp. 571-585. 2018
[58]
J. Xu, C. Zhang, P. Zhang, and D. Song, "Text classification with enriched word features", In: Pacific Rim International Conference on Artificial Intelligence, Springer, pp. 274-281. 2018
[59]
S. Bai, J.Z. Kolter, and V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv: 180301271. 2018
[60]
E. McCrum-Gardner, "Which is the correct statistical test to use?", British J. Oral Maxillofacial Surg., vol. 46, no. 1, pp. 38-41, . 2008
[61]
W.R. Rice, "Analyzing tables of statistical tests", Evolution, vol. 43, no. 1, pp. 223-225, . 1989
[62]
R. Pal, and M. Saraswat, "Data clustering using enhanced biogeography-based optimization", In: 2017 IEEE Tenth International Conference on Contemporary Computing (IC3), pp. 1-6. 2017
[63]
J. Demˇsar, "Statistical comparisons of classifiers over multiple data sets", J. Mach. Learn. Res., vol. 7, no. Jan, pp. 1-30, . 2006
[64]
Y. Hochberg, "A sharper bonferroni procedure for multiple tests of significance", Biometrika, vol. 75, no. 4, pp. 800-802, . 1988
15