Citations


Contact Us

For Marketing, Sales and Subscriptions Inquiries
2637 E Atlantic Blvd #43110
Pompano Beach, FL 33062
USA

Conference List

Review of Computer Engineering Research

June 2019, Volume 6, 2, pp 84-91

A Survey on Sentiment Analysis Algorithms and Datasets

Reena G. Bhati

Reena G. Bhati 1


  1. Computer Science Department, Tilak Maharashtra Vidyapeeth, Pune, India. 1

on Google Scholar
on PubMed

Pages: 84-91

DOI: 10.18488/journal.76.2019.62.84.91

Share :

Article History:

Received: 06 August, 2019
Revised: 10 September, 2019
Accepted: 14 October, 2019
Published: 02 December, 2019


Abstract:

In this paper we present a deep literature review on existing system for sentimental analysis. Basically sentimental analysis (SA) is the measurement of preference of people’s thoughts via natural language processing. The main aim of sentiment analysis is to know the orientation of the sentiment described in script. In recent decades the researcher focuses on the study various algorithms for relevant research results of the sentiment analysis. This research paper provides a comprehensive overview of this field's latest update. In this review, some recent proposed improvements of algorithms and various SA applications are explored and briefly described. The aim of this paper is to provide knowledge about the different method related to sentimental analysis also how they are classified, what the applications of this analysis.
Contribution/ Originality
This study contributes to the existing literature by studying the existing systems and showing the disadvantages and comparative analysis based on various parameters.

Keywords:

Data mining, Natural language processing (NLP), Sentimental analysis (SA), Convolutional neural network (CNN) unsupervised, learning, Text mining, Feature representation.

Reference:

[1]          M.-H. Chen, W.-F. Chen, and L.-W. Ku, "Application of sentiment analysis to language learning," IEEE Access, vol. 6, pp. 24433-24442, 2018. Available at: https://doi.org/10.1109/access.2018.2832137.

[2]          W. Lincy and K. M. Naveen, "A survey on challenges in sentiment analysis," International Journal of Emerging Technology in Computer Science & Electronics, vol. 21, pp. 409-412, 2016.

[3]          P. Chiranjeevi, D. T. Santosh, and B. Vishnuvardhan, Survey on sentiment analysis methods for reputation evaluation. In cognitive informatics and aoft computing. Singapore: Springer, 2019.

[4]          R. Liu, Y. Shi, C. Ji, and M. Jia, "A survey of sentiment analysis based on transfer learning," IEEE Access, vol. 7, pp. 85401-85412, 2019. Available at: 10.1109/ACCESS.2019.2925059.

[5]          P. Priyanka and Y. Pratibha, "Sentiment analysis levels and techniques: A survey," International Journal of Innovations in Engineering and Technology, vol. 6, pp. 523-528, 2016.

[6]          D. Abdullah and J. Anurag, "Survey paper on sentiment analysis: In general terms," International Journal of Emerging Research in Management &Technology, vol. 5, pp. 1093-1113, 2014.

[7]          M. Walaa, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol. 5, pp. 1093-1113, 2014. Available at: https://doi.org/10.1016/j.asej.2014.04.011.

[8]          D. M. E.-D. M. Hussein, "A survey on sentiment analysis challenges," Journal of King Saud University-Engineering Sciences, vol. 30, pp. 330-338, 2018.

[9]          M. Sadegh, R. Ibrahim, and Z. A. Othman, "Opinion mining and sentiment analysis: A survey," International Journal of Computers & Technology, vol. 2, pp. 171-178, 2012.

[10]        A. M. Dudhat, R. R. Badre, and K. Mayura, "A survey on sentiment analysis and opinion mining," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, pp. 6633-6639, 2014.

[11]        M. Al-Ayyoub, A. A. Khamaiseh, Y. Jararweh, and M. N. Al-Kabi, "A comprehensive survey of arabic sentiment analysis," Information Processing & Management, vol. 56, pp. 320-342, 2019. Available at: https://doi.org/10.1016/j.ipm.2018.07.006.

[12]        S. Archana and G. Deipali, "Sentiment analysis and challenges involved: A survey," International Journal of Science and Research, vol. 4, pp. 1928-1932, 2015.

[13]        C. Erion and M. Maurizio, "Word embeddings for sentiment analysis: A comprehensive empirical survey," arXiv:1902.00753v1, DBLP:journals/corr/abs-1902-00753, CoRR , abs/1902.00753, 2019.

[14]        S. Diksha, G. Shubham, J. Joy, and M. Richa, "Sentiment analysis," International Journal of Engineering, Science and Mathematics, vol. 8, pp. 46-52, 2019.

[15]        V. A. Kharde and P. S. Sonawane, "Sentiment analysis of twitter data: A survey of techniques. arXiv:1601.06971. Available: https://arxiv.org/abs/1601.06971 " 2016.

[16]        A. M. Yang, J. H. Lin, Y. M. Zhou, and J. Chen, "Research on building a Chinese sentiment lexicon based on SO-PMI," Applied Mechanics and Materials, vol. 263, pp. 1688-1693, 2013.

[17]        K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," Journal of Big Data, vol. 1, pp. 1-40, 2016.

[18]        P. D. Turney and M. L. Littman, "Measuring praise and criticism: Inference of semantic orientation from association," ACM Transactions on Information Systems, vol. 21, pp. 315-346, 2003. Available at: https://doi.org/10.1145/944012.944013.

[19]        P. Routray, C. Kumar Swain, and S. Praya Mishra, "A survey on sentiment analysis," International Journal of Computer Applications, vol. 76, pp. 1-8, 2013.

[20]        S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, pp. 1345-1359, 2010.

[21]        A. Ruchika and G. Latika, "A hybrid approach for sentiment analysis using classification algorithm," International Journal of Computer Science and Mobile Computing, vol. 6, pp. 149-157, 2017.

[22]        J. Jeevanandam and S. Koteeswaran, "Sentiment analysis: A survey of current research and techniques," International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, pp. 3749-3757, 2015. Available at: https://doi.org/10.15680/ijircce.2015.0305002.

[23]        Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, "Hierarchical attention networks for document classification," in Proceedings of NAACL-HLT, 2016, pp. 1480-1489.

[24]        R. L. Vieriu, A. K. Rajagopal, R. Subramanian, O. Lanz, E. Ricci, N. Sebe, and K. Ramakrishnan, "Boosting-based transfer learning for multi-view head-pose classification from surveillance videos," in Proc. 20th Eur. Signal Process. Conf. (EUSIPCO), Aug, 2012, pp. 649-653.

[25]        Y. Kim, "Convolutional neural networks for sentence classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1746-1751.

[26]        B. McCann, J. Bradbury, C. Xiong, and R. Socher, "Learned in translation: Contextualized word vectors," in Advances in Neural Information Processing Systems, 2017, pp. 6294-6305.

[27]        M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, "Deep contextualized word representations," arXiv preprint arXiv:1802.05365, DBLP:journals/corr/abs-1802-05365, CoRR, volume = abs/1802.05365, 2018.

[28]        A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun, "Very deep convolutional networks for text classification," arXiv preprint arXiv:1606.01781, CoRR volume = abs/1606.01781, 2016.

[29]        R. Johnson and T. Zhang, "Supervised and semi-supervised text categorization using LSTM for region embeddings," arXiv preprint arXiv:1602.02373, 2016.

[30]        A. M. Dai and Q. V. Le, "Semi-supervised sequence learning," in Processing Advances in Neural Information Processing Systems, 2015, pp. 3079-3087.

[31]        J. Howard and S. Ruder, "Universal language model fine-tuning for text classification," in arXiv preprint arXiv:1801.06146, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), Melbourne, Australia, Association for Computational Linguistic, 2018, pp. 328–339.

[32]        A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, "Learning word vectors for sentiment analysis," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.

[33]        R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, "Recursive deep models for semantic compositionality over a sentiment treebank," in Proceedings of the Conference Empirical Methods Natural Language Process, 2013, pp. 1631-642.

[34]        J. Blitzer, M. Dredze, and F. Pereira, "Domain adaptation for sentiment classification," in Proceedings of the 45th Annual Meeting of the Association Computational Linguistics, 2007.

[35]        A. Go, R. Bhayani, and L. Huang, "Twitter sentiment classification using distant supervision," Stanford, CA, USA, Tech. Rep. CS224N, vol. 1, 2009.

Statistics:

Google Scholor ideas Microsoft Academic Search bing Google Scholor

Funding:

This study received no specific financial support.

Competing Interests:

The author declares that there are no conflicts of interests regarding the publication of this paper.

Acknowledgement:


Related Article