Sentimental analysis using recurrent neural network

  • Authors

    • Merin Thomas VTU
    • Latha C.A VTU
    2018-08-02
    https://doi.org/10.14419/ijet.v7i2.27.12635
  • Sentimental Analysis, Deep Learning, Neural Network, Recurrent Neural Network.

  • Sentiment analysis has been an important topic of discussion from two decades since Lee published his first paper on the sentimental analysis in 2002. Apart from the sentimental analysis in English, it has spread its wing to other natural languages whose significance is very important in a multi linguistic country like India. The traditional approaches in machine learning have paved better accuracy for the Analysis. Deep Learning approaches have gained its momentum in recent years in sentimental analysis. Deep learning mimics the human learning so expectations are to meet higher levels of accuracy. In this paper we have implemented sentimental analysis of tweets in South Indian language Malayalam. The model used is Recurrent Neural Networks Long Short-Term Memory, a deep learning technique to predict the sentiments analysis. Achieved accuracy was found increasing with quality and depth of the datasets.

     

  • References

    1. [1] Ali, Aasim, ShahidSiddiq, and Muhammad Kamran Malik. "Development of parallel corpus and english to urdu statistical machine translation." Int. J. of Engineering & Technology IJET-IJENS 10 (2010): 31-33.

      [2] DEEP, Kamal; KUMAR, Ajit; GOYAL, Vishal. Development of Punjabi-English (PunEng) Parallel Corpus for Machine Translation System. International Journal of Engineering & Technology, [S.l.], v. seven, n. two, p. 690-693, may 2018. ISSN 2227-524X.

      [3] Du, Jiali, Pingfang Yu, and Minglin Li. "Machine Learning from Garden Path Sentences: The Application of Computational Linguistics." International Journal of Emerging Technologies in learning (iJET) 9.6 (2014): 58-62https://doi.org/10.3991/ijet.v9i6.4109.

      [4] PrabhuPalanisamy, Vineet Yadav and HarshaElchuri, (2013). “Serendio Simple and Practical Lexicon Based approach to Sentiment analysis,†Volume 2: Seventh International Workshop on Semantic Evaluation, Atlanta, Georgia, pages 543–548.

      [5] I.Hemalatha, Dr.G.PSaradhi Varma, Dr.A.Govardhan(2013), “Sentiment analysis tool using machine learning algorithmâ€, volume 2, Issue 2, International journal of emerging Trends and Technology in computer.

      [6] Peter D.Turney, (2002), “Thumbs up and thumbs down? Semantic Orientation Applied to Unsupervised Classificationâ€, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, 417-424.

      [7] Bo Pang, Lillian Lee, and ShivakumarVaithyanathan. 2002. Thumbs up: sentiment classification using machine-learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86.https://doi.org/10.3115/1118693.1118704.

      [8] Amolik, Akshay, et al. "Twitter sentiment analysis of movie reviews using machine learning techniques." International Journal of Engineering and Technology 7.6 (2016): 1-7.

      [9] Sida Wang and Christopher D Manning. 2012. Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pages 90–94.

      [10] Asha, T., et al. "Building machine learning algorithms on Hadoop for bigdata." International Journal of Engineering and Technology 3.2 (2013).

      [11] Hanafiah, Novita, et al. "Text Normalization Algorithm on Twitter in Complaint Category." Procedia Computer Science116 (2017): 20-26.https://doi.org/10.1016/j.procs.2017.10.004.

      [12] Sreelekha, S., Pushpak Bhattacharyya, and D. Malathi. "A case study on englishmalayalam machine translation." Proceedings of the iDravidian (2015).

      [13] YoshuaBengio, RejeanDucharme, Pascal Vincent, and ´ Christian Janvin. 2003. A neural probabilistic language model. The Journal of Machine Learning Research, 3:1137–1155

      [14] Ronan Collobert, Jason Weston, Leon Bottou, Michael ´ Karlen, KorayKavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493–2537.

      [15] Satyanarayana P, Charishma Devi, Sowjanya P, Satish Babu, Syam Kumar, Implementation of conventional communication system in deep learning.International Journal of Engineering & Technology v.7 (1.1) p. 696-698, 2018. ISSN 2227-524X.

      [16] Collobert, Ronan, and Jason Weston. "A unified architecture for natural language processing: Deep neural networks with multitask learning." Proceedings of the 25th international conference on Machine learning. ACM, 2008.https://doi.org/10.1145/1390156.1390177.

      [17] Hassan, Abdalraouf, and Ausif Mahmood. "Convolutional Recurrent Deep Learning Model for Sentence Classification." IEEE Access 6 (2018): 13949-13957.https://doi.org/10.1109/ACCESS.2018.2814818.

      [18] Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan ´ Cernocky, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, September 26-30, 2010, pages 1045–1048.

      [19] EPhzibah, E. P., and R. Sujatha. "Big data management with machine learning inscribed by domain knowledge for health care." International Journal of Engineering & Technology 6.4 (2017): 98-102.https://doi.org/10.14419/ijet.v6i4.8214.

      [20] Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), volume 1631, and pages 1631–1642.

      [21] OzanIrsoy and Claire Cardie. 2014. Deep recursive neural networks for compositionality in language. In Advances in Neural Information Processing Systems, pages 2096–2104.

      [22] Sepp Hochreiter and JurgenSchmidhuber. 1997. ¨ long short-term memory. Neural computation, 9(8):1735–1780.https://doi.org/10.1162/neco.1997.9.8.1735.

      [23] Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075.

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    Thomas, M., & C.A, L. (2018). Sentimental analysis using recurrent neural network. International Journal of Engineering & Technology, 7(2.27), 88-92. https://doi.org/10.14419/ijet.v7i2.27.12635