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Natural Language Processing and Bi-Directional LSTM for Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 27 |
| Year of Publication: 2023 |
| Authors: Mohamed Muftah Abubaera, Salman Mohammed Jiddah |
10.5120/ijca2023923021
|
Mohamed Muftah Abubaera, Salman Mohammed Jiddah . Natural Language Processing and Bi-Directional LSTM for Sentiment Analysis. International Journal of Computer Applications. 185, 27 ( Aug 2023), 31-35. DOI=10.5120/ijca2023923021
Abstract
In e-commerce, one of the most critical and important aspects of the business model is customer reviews. Customer reviews reflect the satisfaction of customers with respect to the products and services offered. E-commerce is driven by significant amounts of data which poses a huge challenge of collection and evaluation to have an insight before decision-making and business strategy implementations. The field of natural language processing and machine learning techniques have provided significant leaps in helping the analysis of big data and business analytics. Also, Recurrent Neural Networks (RNN) evolved in so many powerful algorithms and one of those is the Bi-LSTM variation of RNNs. Bi-LSTM has been identified in the literature as a suitable machine learning classification algorithm for natural language processing due to its sequential learning process. This study is an implementation of the lemmatization natural language processing technique coupled with the Bi-LSTM machine learning classification technique for customer review sentiment analysis. The application of these two techniques has reported a significant performance accuracy in sentiment analysis of customer review data. The results in this study are reported as 96.06%, 91%, and 90% for accuracy, precision, and recall respectively.
References
- Banerjee, I., Ling, Y., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., ... & Lungren, M. P. (2019). Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artificial intelligence in medicine, 97, 79-88.
- Boban, I., Doko, A., & Gotovac, S. (2020). Sentence retrieval using stemming and lemmatization with different length of the queries. Advances in Science, Technology and Engineering Systems, 5(3), 349-354.
- Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649.
- Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
- Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), 483.
- Du, C., Sun, H., Wang, J., Qi, Q., Liao, J., Xu, T., & Liu, M. (2019, November). Capsule network with interactive attention for aspect-level sentiment classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 5489-5498).
- Haque, T. U., Saber, N. N., & Shah, F. M. (2018, May). Sentiment analysis on large scale Amazon product reviews. In 2018 IEEE international conference on innovative research and development (ICIRD) (pp. 1-6). IEEE.
- Hawlader, M., Ghosh, A., Raad, Z. K., Chowdhury, W. A., Shehan, M. S. H., & Ashraf, F. B. (2021, September). Amazon Product Reviews: Sentiment Analysis Using Supervised Learning Algorithms. In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT) (pp. 1-6). IEEE.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
- Jiddah, S. M., Abushakra, M., & Yurtkan, K. (2021). Fusion of geometric and texture features for side-view face recognition using svm. Istatistik Journal of The Turkish Statistical Association, 13(3), 108-119.
- Katić, T., & Milićević, N. (2018, September). Comparing sentiment analysis and document representation methods of amazon reviews. In 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000283-000286). IEEE.
- Kaur, M., & Mohta, A. (2019, November). A review of deep learning with recurrent neural network. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 460-465). IEEE.
- Kumar, H. M., Harish, B. S., & Darshan, H. K. (2019). Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method. International Journal of Interactive Multimedia & Artificial Intelligence, 5(5).
- Murthy, A. R., & Kumar, K. A. (2021, March). A review of different approaches for detecting emotion from text. In IOP Conference Series: Materials Science and Engineering (Vol. 1110, No. 1, p. 012009). IOP Publishing.
- Otter, D. W., Medina, J. R., & Kalita, J. K. (2020). A survey of the usages of deep learning for natural language processing. IEEE transactions on neural networks and learning systems, 32(2), 604-624.
- Pal, S., Ghosh, S., & Nag, A. (2018). Sentiment analysis in the light of LSTM recurrent neural networks. International Journal of Synthetic Emotions (IJSE), 9(1), 33-39.
- Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre-trained models for natural language processing: A survey. Science China Technological Sciences, 63(10), 1872-1897.
- Ruder, S., Peters, M. E., Swayamdipta, S., & Wolf, T. (2019, June). Transfer learning in natural language processing. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Tutorials (pp. 15-18).
- Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
- Shrestha, N., & Nasoz, F. (2019). Deep learning sentiment analysis of amazon. com reviews and ratings. arXiv preprint arXiv:1904.04096.
- Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Rush, A. M. (2020, October). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations (pp. 38-45).
- Yang, W., Lu, W., & Zheng, V. W. (2019). A simple regularization-based algorithm for learning cross-domain word embeddings. arXiv preprint arXiv:1902.00184.
- Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
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