International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 5 (2018)

Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature

Published
2018-02-20

Abstract

The exponential growth of social evaluations of services has led many researchers to focus on emotion analysis. Having so much data helps analyze end-user behavior to improve QoS. Text categorization is a prominent language processing research area that organizes the disorganized text into useable categories. The LSTM and CNN models are widely employed in text-based categorization applications and produce reliable results. By contrast, LSTM-based models gain long-term connections between text sequences and so are better suitable for text classification. In this case, the hybrid approach may memorize classification, slowing down the training process. This work proposes an optimal attention-focused BiLSTM and ConvNet model. The suggested model is trained on two independent datasets to validate its performance. The proposed attention-based model outperformed previous deep learning algorithms. Compared to existing machine learning methods, the proposed approach is more accurate.

References

  1. Adusumalli, H. P. (2016a). Digitization in Production: A Timely Opportunity. Engineering International, 4(2), 73-78. https://doi.org/10.18034/ei.v4i2.595
  2. Adusumalli, H. P. (2016b). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
  3. Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58
  4. Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618
  5. Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597
  6. Chen H., Sun M., Tu C., Lin Y., and Liu Z., “Neural sentiment classification with user and product attention,” in Proc. Conf. Empirical Methods Natural Lang. Process., 2016, pp. 1650– 1659.
  7. Chen, H.; Sun, M.; Tu, C.; Lin, Y.; Liu, Z. Neural sentiment classification with user and product attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA, 21 September 2016; pp. 1650–1659.
  8. Dou, Z.Y. Capturing user and product Information for document level sentiment analysis with deep memory network. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 3 March 2017; pp. 521–526.
  9. Jiang L., Yu M., Zhou M., Liu X., and Zhao T.: Target-dependent Twitter sentiment classification. in Proc. ACL, 2011, pp. 151–160.
  10. Lin, Z.; Feng, M.; Santos CN, D.; Yu, M.; Xiang, B.; Zhou, B.; Bengio, Y. A structured self-attentive sentence embedding. arXiv preprint 2017, arXiv: 1703.03130.
  11. Pasupuleti, M. B. (2015a). Data Science: The Sexiest Job in this Century. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 8–11. Retrieved from https://upright.pub/index.php/ijrsps/article/view/56
  12. Pasupuleti, M. B. (2015b). Problems from the Past, Problems from the Future, and Data Science Solutions. ABC Journal of Advanced Research, 4(2), 153-160. https://doi.org/10.18034/abcjar.v4i2.614
  13. Pasupuleti, M. B. (2015c). Stimulating Statistics in the Epoch of Data-Driven Innovations and Data Science. Asian Journal of Applied Science and Engineering, 4, 251–254. Retrieved from https://upright.pub/index.php/ajase/article/view/55
  14. Pasupuleti, M. B. (2016a). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617
  15. Pasupuleti, M. B. (2016b). The Use of Big Data Analytics in Medical Applications. Malaysian Journal of Medical and Biological Research, 3(2), 111-116. https://doi.org/10.18034/mjmbr.v3i2.615
  16. Perez-Rosas V., Banea C., and Mihalcea R.: Learning sentiment lexicons in Spanish. LREC, vol. 12, p. 73, May 2012
  17. Ren Y., Zhang Y., Zhang M., and Ji D.. Context-sensitive Twitter sentiment classification using neural network. in Proc. AAAI, Phoenix, AZ, USA, Feb. 2016, pp. 215_221.
  18. Rosenthal S., Farra N., and Nakov P.. SemEval-2017 task 4: Sentiment analysis in Twitter. Proc. SemEval, Vancouver, BC, Canada, Aug. 2017,pp. 502_518.
  19. Tang D., Qin B., and Liu T.: Aspect level sentiment classification with deep memory network. Proc. Conf. Empirical Methods Natural Lang. Process. 2016, pp. 214_224.
  20. Tang D., Qin B., Feng X., and Liu T.: Effective LSTMs for target-dependent sentiment classification. in Proc. 26th Int. Conf. Comput.Linguistics, Tech. Papers (COLING), 2016, pp. 3298_3307.
  21. Yang Z., Yang D., Dyer C., He X., Smola A., and Hovy E.. Hierarchical attention networks for document classification,” in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum.Lang. Technol., 2016, pp. 1480–1489.
  22. Zhang M., Zhang Y., and Vo D. T.: Gated neural networks for targeted sentiment analysis. Proc. AAAI Conf. Artif. Intell., 2016, pp. 3087–3093

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