International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Deep Learning Model for Extracting Consumer Sentiments using Recurrent Neural Network Techniques

  • Ranjan, Roop (Madan Mohan Malaviya University of Technology) ;
  • Daniel, AK (Madan Mohan Malaviya University of Technology)
  • Received : 2021.08.05
  • Published : 2021.08.30
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Abstract

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

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References

  1. J. Zhang and C. Zong, "Deep neural networks in machine translation: An overview," IEEE Intell. Syst., vol. 30, no. 5, pp. 16-25, Sep./Oct. 2015. https://doi.org/10.1109/MIS.2015.69
  2. W. Yin, H. Schutze, B. Xiang, and B. Zhou, "ABCNN: Attention-based convolutional neural network for modeling sentence pairs," Trans. Assoc. Comput. Linguistics, vol. 4, pp. 259-272, Dec. 2016. https://doi.org/10.1162/tacl_a_00097
  3. Ansari, M. Maknojia, and A. Shaikh, "Intelligent question answering system based on artificial neural network," in Proc. IEEE ICETECH, Coimbatore, India, Mar. 2016, pp. 758-763.
  4. M. Rush, S. Chopra, and J. Weston, "A neural attention model for abstractive sentence summarization," 2015, arXiv:1509.00685. [Online]. Available: https://arxiv.org/abs/1509.00685
  5. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," J. Mach. Learn. Res., vol. 12 pp. 2493_2537, Aug. 2011.
  6. O. Levy, Y. Goldberg, and I. Dagan, "Improving distributional similarity with lessons learned from word embeddings," Trans. Assoc. Comput.Linguistics, vol. 3, pp. 211_225, May 2015. https://doi.org/10.1162/tacl_a_00134
  7. Q. Huang, R. Chen, X. Zheng, and Z. Dong, "Deep sentiment representation based on CNN and LSTM," in Proc. Int. Conf. Green Informat., Aug. 2017, pp. 30_33.
  8. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Netw., vol. 61, pp. 85_117, Jan. 2015. https://doi.org/10.1016/j.neunet.2014.09.003
  9. L. Brocki and K. Marasek, "Deep belief neural networks and bidirectional long-short term memory hybrid for speech recognition," Arch. Acoust., vol. 40, no. 2, pp. 191_195, Jun. 2015. https://doi.org/10.1515/aoa-2015-0021
  10. V. Campos, B. Jou, and X. Giro-i-Nieto, "From pixels to sentiment: Fine tuning CNNs for visual sentiment prediction," Image Vis. Comput., vol. 65, pp. 15_22, Sep. 2017 https://doi.org/10.1016/j.imavis.2017.01.011
  11. H.-F. Tang, S.-B. Tan, and X.-Q. Cheng, "Research on sentiment classication of chinese reviews based on supervised machine learning techniques,"J. Chin. Inf. Process., vol. 21, no. 6, pp. 88_126, 2007. https://doi.org/10.3969/j.issn.1003-0077.2007.06.012
  12. G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, "Sentiment analysis of comment texts based on BiLSTM," IEEE Access, vol. 7, pp. 51522_51532,2019. https://doi.org/10.1109/ACCESS.2019.2909919
  13. G. Liu and J. Guo, "Bidirectional LSTM with attention mechanism and convolutional layer for text classi_cation," Neurocomputing, vol. 337, pp. 325_338, Apr. 2019. https://doi.org/10.1016/j.neucom.2019.01.078
  14. F. Y. Zhou, L. P. Jin, and J. Dong, "Review of convolutional neural network," Chin. J. Comput., vol. 1, pp. 35_38, Jan. 2017.
  15. Official website of Keras," https://keras.io/about/"
  16. Ranjan R., Daniel A.K. (2021), "Intelligent Sentiments Information Systems Using Fuzzy Logic", Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_55
  17. L.-C.Yu, L.-H. Lee, S. Hao, J.Wang,Y. He, J. Hu, K. R. Lai, and X. Zhang, "Building Chinese affective resources in valence-arousal dimensions," in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang.Technol. (NAACL-HLT), 2016, pp. 540-545.
  18. J.-L. Wu, L.-C. Yu, and P.-C. Chang, "Emotion classi_cation by removal of the overlap from incremental association language features," J. Chin. Inst. Engineers, vol. 34, no. 7, pp. 947_955, Oct. 2011. https://doi.org/10.1080/02533839.2011.591964
  19. Z. Cui, X. Shi, and Y. Chen, "Sentiment analysis via integrating distributed representations of variable-length word sequence," Neurocomputing, vol. 187, pp. 126_132, Apr. 2016. https://doi.org/10.1016/j.neucom.2015.07.129
  20. M. Abdel Fattah, "New term weighting schemes with combination of multiple classifiers for sentiment analysis," Neurocomputing, vol. 167, pp. 434_442, Nov. 2015. https://doi.org/10.1016/j.neucom.2015.04.051
  21. H. Xia, Y. Yang, X. Pan, Z. Zhang, and W. An, "Sentiment analysis for online reviews using conditional random fields and support vector machines," Electron. Commerce Res., pp. 1_18, May 2019, doi:10.1007/s10660-019-09354-7.
  22. O. Irsoy and C. Cardie, "Opinion mining with deep recurrent neural networks," presented at the Conference Empirical Methods Natural Language Processing (EMNLP), 2014.
  23. P. Liu, S. Joty, and H. Meng, "Fine-grained opinion mining with recurrent neural networks and word embeddings," presented at the Conference Empirical Methods Natural Language Processing, 2015
  24. D. Tang, B. Qin, X. Feng, and T. Liu, "Effective LSTMs for target dependent sentiment classification," in Proc. 26th Int. Conf. Comput. Linguistics (COLING), Osaka, Japan, 2016, pp. 3298-3307.
  25. M. Zhang, Y. Zhang, and D. Tang, "Gated neural networks for targeted sentiment analysis," in Proc. 30th AAAI Conf. Artif. Intell., Phoenix, AZ, USA, 2016, pp. 3087-3093.
  26. S. K. Sonderby, C. K. Sonderby, H. Nielsen, and O. Winther, "Convolutional LSTM networks for subcellular localization of proteins," in Proc. 2nd Int. Conf. Algorithms Comput. Biol. Mexico City, Mexico: Springer-Verlag, 2015, pp. 68-80.
  27. J. Wang, L.-C. Yu, K. R. Lai, and X. Zhang, "Dimensional sentiment analysis using a regional CNN-LSTM model," in Proc. 54th Annu. Meeting Assoc. Comput. Linguistics, Berlin, Germany, 2016, pp. 225-230.
  28. Y. Wang, M. Huang, X. Zhu, and L. Zhao, "Attentionbased LSTM for aspect-level sentiment classification," in Proc. EMNLP, 2016, pp. 606-615
  29. R. Socher, "Recursive deep models for semantic compositionality over a sentiment treebank," in Proc. Conf. Empirical Methods Natural Lang. Process., Seattle, WA, USA, 2013, pp. 1631-1642.
  30. D. Santos and M. Gattit, "Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts," in Proc. 25th Int. Conf. Comput. Linguistics: Tech. Papers, Dublin, Ireland, 2014, pp. 69-78.
  31. Baziotis, N. Pelekis, and C. Doulkeridis, "Datastories at SemEval2017 Task 4: Deep LSTM with attention for message-level and topicbased sentiment analysis," in Proc. 11th Int. Workshop Semantic Eval. (SemEval), Vancouver, BC, Canada, Aug. 2017, pp. 747-754.
  32. S. Amir, C. B. Wallace, H. Lyu, P. Carvalho, and J. M. Silva, "Modelling context with user embeddings for sarcasm detection in social media," in Proc. CONLL 2016.
  33. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11,pp. 2278_2324, Nov. 1998. https://doi.org/10.1109/5.726791
  34. Roop Ranjan, AK Daniel, Emotion Detection for Travelling Services using Rule-Based Fuzzy Inference System, Springer International Conference on Machine Intelligence and Smart Systems (MISS2020), Rustamji Institute of Technology, Tekanpur, Gwalior, M.P., September 24-25, 2020.
  35. Krishnani, A. Kumari, A. Dewangan, A. Singh and N. S. Naik, "Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms," TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON), 2019, pp. 367-372, doi: 10.1109/TENCON.2019.8929434.