Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. These kinds of data assets, which are a broad stage of people`s sentiment, suggestions, input, and audits, are viewed as intense witnesses and have become a valuable resource for big industries, research and technology markets, news service providers, and numerous domains where sentiment analysis became a useful tool. This paper discusses on deep learning algorithms applied in recent years for sentiment analysis. The main goal of this paper is to analyze how deep learning research is growing in different application areas and can be helpful for sentiment analysis.

Computer Vision, Deep Learning, Machine Learning, Natural Language Processing Sentiment Analysis

[1] Miftah Andriansyah et.al, “Comparative Study: The Implementation of Machine Learning Method for Sentiment Analysis in Social Media. A Recommendation for Future Research,” Adv. Sci. Lett., vol. 20, no. No. 10/11/12, pp. 2009–2013, 2014.
[2] O. Araque, I. Corcuera-Platas, J. F. Sánchez-Rada, and C. A. Iglesias, “Enhancing deep learning sentiment analysis with ensemble techniques in social applications,” Expert Syst. Appl., vol. 77, pp. 236–246, Jul. 2017.
[3] A. Balahur and M. Turchi, “Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis,” Comput. Speech Lang., vol. 28, no. 1, pp. 56–75, Jan. 2014.
[4] S. Poria, H. Peng, A. Hussain, N. Howard, and E. Cambria, “Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis,” Neurocomputing, vol. 261, pp. 217–230, Oct. 2017.
[5] E. Fersini, “Sentiment Analysis in Social Networks,” in Sentiment Analysis in Social Networks, Elsevier, 2017, pp. 91–111.
[6] R. Arulmurugan, K. R. Sabarmathi, and H. Anandakumar, “Classification of sentence level sentiment analysis using cloud machine learning techniques,” Cluster Comput., Sep. 2017.
[7] M. Biba and M. Mane, “Sentiment Analysis through Machine Learning: An Experimental Evaluation for Albanian,” 2014, pp. 195–203.
[8] B. L. Nitin Jindal, “Mining comparative sentences and relations,” in AAAI’06 proceedings of the 21st national conference on Artificial intelligence, 2006, pp. 1331–1336.
[9] C. (eds. . Aggarwal, C.C., Zhai, Mining Text Data. Springer, 2012.
[10] A. Yousif, Z. Niu, J. K. Tarus, and A. Ahmad, “A survey on sentiment analysis of scientific citations,” Artif. Intell. Rev., Dec. 2017.
[11] R. Pimprikar, S. Ramachadran, and K. Senthilkumar, “Use of machine learning algorithms and twitter sentiment analysis for stock market prediction,” Int. J. Pure Appl. Math., vol. 115, no. 6, pp. 521–526, 2017.
[12] Y. Lee, H. Ryu, and H. Lee, “Stock prediction and prediction accuracy improvement using sentiment analysis and machine learning based on online news,” Proc. Int. Conf. Ind. Eng. Oper. Manag., pp. 1338–1349, 2017.
[13] N. C. Petersen and J. Villadsen, “Combining formal logic and machine learning for sentiment analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8502 LNAI, pp. 375–384, 2014.
[14] B. Le and H. Nguyen, “Twitter Sentiment Analysis Using Machine Learning Techniques,” 2015, pp. 279–289.
[15] T. Huynh, Y. He, and R. Stefan, “Learning Higher-Level Features with Convolutional Restricted Boltzmann Machines for Sentiment Analysis,” Adv. Inf. Retr., pp. 447–452, 2015.
[16] S. Mahalakshmi and E. Sivasankar, “Cross Domain Sentiment Analysis Using Different Machine Learning Techniques,” 2015, pp. 77–87.
[17] M. Hammad and M. Al-awadi, “Sentiment Analysis for Arabic Reviews in Social Networks Using Machine Learning,” 2016, pp. 131–139.
[18] D. Nguyen, K. Vo, D. Pham, M. Nguyen, and T. Quan, “A Deep Architecture for Sentiment Analysis of News Articles,” 2018, pp. 129–140.
[19] N. Abdelhade, T. H. A. Soliman, and H. M. Ibrahim, “Detecting Twitter Users’ Opinions of Arabic Comments During Various Time Episodes via Deep Neural Network,” 2018, pp. 232–246.
[20] A. P. Patil, D. Doshi, D. Dalsaniya, and B. S. Rashmi, “Applying Machine Learning Techniques for Sentiment Analysis in the Case Study of Indian Politics,” 2018, pp. 351–358.
[21] M. Gridach, H. Haddad, and H. Mulki, “Empirical Evaluation of Word Representations on Arabic Sentiment Analysis,” 2018, pp. 147–158.
[22] N. Haldenwang, K. Ihler, J. Kniephoff, and O. Vornberger, “A Comparative Study of Uncertainty Based Active Learning Strategies for General Purpose Twitter Sentiment Analysis with Deep Neural Networks,” 2018, pp. 208–215.
[23] N. C. Petersen and J. Villadsen, “Logical Entity Level Sentiment Analysis,” 2018, pp. 54–71.
[24] S. Sarkar, P. Mallick, and A. Banerjee, “A Real-Time Machine Learning Approach for Sentiment Analysis,” 2015, pp. 705–717.
[25] M. M. Altawaier and S. Tiun, “Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 6, no. 6, p. 1067, 2016.
[26] M. S. Neethu and R. Rajasree, “Sentiment analysis in twitter using machine learning techniques,” 2013 Fourth Int. Conf. Comput. Commun. Netw. Technol., pp. 1–5, 2013.
[27] G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis,” in 2014 Seventh International Conference on Contemporary Computing (IC3), 2014, pp. 437–442.
[28] D. S. Nair, J. P. Jayan, Rajeev R.R, and E. Sherly, “Sentiment Analysis of Malayalam film review using machine learning techniques,” in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015, pp. 2381–2384.
[29] V. A. Rohani and S. Shayaa, “Utilizing machine learning in Sentiment Analysis: SentiRobo approach,” in 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET), 2015, pp. 263–267.
[30] M. Moh, A. Gajjala, S. C. R. Gangireddy, and T.-S. Moh, “On Multi-tier Sentiment Analysis Using Supervised Machine Learning,” in 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015, pp. 341–344.
[31] M. Ashok, S. Rajanna, P. V. Joshi, and Sowmya Kamath S, “A personalized recommender system using Machine Learning based Sentiment Analysis over social data,” in 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), 2016, pp. 1–6.
[32] E. Aydogan and M. A. Akcayol, “A comprehensive survey for sentiment analysis tasks using machine learning techniques,” in 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), 2016, pp. 1–7.
[33] S. Garg, A. Saini, and N. Khanna, “Is sentiment analysis an art or a science? Impact of lexical richness in training corpus on machine learning,” in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016, pp. 2729–2735.
[34] V. Rohini, M. Thomas, and C. A. Latha, “Domain based sentiment analysis in regional Language-Kannada using machine learning algorithm,” in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016, pp. 503–507.
[35] N. Bansal and A. Singh, “A review on opinionated sentiment analysis based upon machine learning approach,” in 2016 International Conference on Inventive Computation Technologies (ICICT), 2016, pp. 1–6.
[36] U. A. Siddiqua, T. Ahsan, and A. N. Chy, “Combining a rule-based classifier with ensemble of feature sets and machine learning techniques for sentiment analysis on microblog,” in 2016 19th International Conference on Computer and Information Technology (ICCIT), 2016, pp. 304–309.
[37] N. Wang, B. Varghese, and P. D. Donnelly, “A machine learning analysis of Twitter sentiment to the Sandy Hook shootings,” in 2016 IEEE 12th International Conference on e-Science (e-Science), 2016, pp. 303–312.
[38] V. S. Rajput and S. M. Dubey, “Stock market sentiment analysis based on machine learning,” in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), 2016, pp. 506–510.
[39] X. Zhang and X. Zheng, “Comparison of Text Sentiment Analysis Based on Machine Learning,” in 2016 15th International Symposium on Parallel and Distributed Computing (ISPDC), 2016, pp. 230–233.
[40] A. Hassan and A. Mahmood, “Deep Learning approach for sentiment analysis of short texts,” in 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017, pp. 705–710.
[41] M. Dragoni and G. Petrucci, “A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis,” IEEE Trans. Affect. Comput., vol. 8, no. 4, pp. 457–470, Oct. 2017.
[42] J. Wehrmann, W. Becker, H. E. L. Cagnini, and R. C. Barros, “A character-based convolutional neural network for language-agnostic Twitter sentiment analysis,” in 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 2384–2391.
[43] B. Roshanfekr, S. Khadivi, and M. Rahmati, “Sentiment analysis using deep learning on Persian texts,” in 2017 Iranian Conference on Electrical Engineering (ICEE), 2017, pp. 1503–1508.
[44] 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, p. 3, Dec. 2018.
[45] Y. Chen and Z. Zhang, “Research on text sentiment analysis based on CNNs and SVM,” in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018, pp. 2731–2734.
[46] Y. Chen, B. Zhou, W. Zhang, W. Gong, and G. Sun, “Sentiment Analysis Based on Deep Learning and Its Application in Screening for Perinatal Depression,” in 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 2018, pp. 451–456.
[47] 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, Jul. 2018.
[48] Shrija Madhu, "An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue 4, pp. 34-36, 2018