Optimized RMDL with Transfer Learning for Sentiment Classification in the MapReduce Framework
DOI:
https://doi.org/10.13052/jwe1540-9589.2282Keywords:
MapReduce, deep learning, review document, AlexNet, ResNet 50Abstract
A core task in sentiment analysis is sentiment categorization, and it is crucial to understand user feelings based on their remarks in social media or product evaluations. Due to ambiguous phrases, refusal words, and other factors, categorizing sentiment presents several challenging issues. The objective of this research is to develop a hybrid optimization-based deep learning model and MapReduce framework-based sentiment categorization approach. The review document is taken from a dataset and used in this case with the MapReduce methodology. MapReduce is a software framework and programming model for analyzing massive volumes of data that consists of two phases, mapper and reducer. BERT tokenization and aspect term extraction are executed in the mapper phase, whereas sentiment analysis is performed in the reducer stage utilizing random multimodal deep learning (RMDL) with transfer learning and AlexNet and ResNet 50 as pre-trained models. In addition, the exponential coot political algorithm (ECPA) is offered as an optimization algorithm for weight optimization in RMDL. The ECPA is obtained by combining the exponential weighted moving average model (EWMA) with the coot algorithm, as well as a political optimizer (PO). The proposed ECPA_RMDL model has acquired 90.9% precision, 89.7% recall, and 89.9% f-measure.
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M.K. Kurian, S. Vishnupriya, R. Ramesh, G. Divya, and D. Divya, “Big data sentiment analysis using Hadoop”, International Journal for Innovative Research in Science and Technology, vol. 1, no. 11, pp. 92–96, 2015.
M. Poongothai, and M. Sangeetha, “Chronological-brain storm optimization-based support vector neural network for sentiment classification using map reduce framework”, Sādhanā, vol. 45, pp. 1–12, 2020.
Z. Xiao, X. Li, L. Wang, Q. Yang, J. Du, and A.K. Sangaiah, “Using convolution control block for Chinese sentiment analysis”, Journal of Parallel and Distributed Computing, vol. 116, pp. 18–26, 2018.
N. Jindal, and B. Liu, “Opinion spam and analysis”, In Proceedings of the 2008 international conference on web search and data mining, pp. 219–230, February, 2008.
G. Zhang, C.H.R. Hsu, H. Lai, and X. Zheng, “Deep learning-based feature representation for automated skin histopathological image annotation”, Multimedia Tools and Applications, vol. 77, pp. 9849–9869, 2018.
J. Guo, “Deep learning approach to text analysis for human emotion detection from big data”, Journal of Intelligent Systems, vol. 31, no. 1, pp. 113–126, 2022.
Y. Ma, K.L. Nguyen, F.Z. Xing, and E. Cambria, “A survey on empathetic dialogue systems”, Information Fusion, vol. 64, pp. 50–70, 2020.
W. Li, W. Shao, S. Ji, and E. Cambria, “BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis”, Neurocomputing, vol. 467, pp. 73–82, 2022.
I. Chaturvedi, E. Ragusa, P. Gastaldo, R. Zunino, and E. Cambria, “Bayesian network based extreme learning machine for subjectivity detection”, Journal of The Franklin Institute, vol. 355, no. 4, pp. 1780–1797, 2018.
M.E. Basiri, S. Nemati, M. Abdar, E. Cambria, and U.R. Acharya, “ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis”, Future Generation Computer Systems, vol. 115, pp. 279–294, 2021.
A. Gepperth, and B. Hammer, “Incremental learning algorithms and applications”, In European symposium on artificial neural networks (ESANN), 2016.
Y. Goldberg, “A primer on neural network models for natural language processing”, Journal of Artificial Intelligence Research, vol. 57, pp. 345–420, 2016.
L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1253, 2018.
Z. Hameed, and B. Garcia-Zapirain, “Sentiment classification using a single-layered BiLSTM model”, Ieee Access, vol. 8, pp. 73992–74001, 2020.
T.H. Nguyen, and K. Shirai, “Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis”, In Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 2509–2514, September, 2015.
B. Liang, H. Su, L. Gui, E. Cambria, and R. Xu, “Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks”, Knowledge-Based Systems, vol. 235, pp. 107643, 2022.
B. Liu, “Text sentiment analysis based on CBOW model and deep learning in big data environment”, Journal of ambient intelligence and humanized computing, vol. 11, pp. 451–458, 2020.
A. Alsayat, “Improving sentiment analysis for social media applications using an ensemble deep learning language model”, Arabian Journal for Science and Engineering, vol. 47, no. 2, pp. 2499–2511, 2022.
R.K. Thakur and M.V. Deshpande, “Kernel Optimized-Support Vector Machine and Mapreduce framework for sentiment classification of train reviews”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 27, no. 06, pp. 1025–1050, December 2019.
I. Wahlang, P. Sharma, S. Sanyal, G. Saha, and A.K. Maji, “Deep learning techniques for classification of brain MRI”, International Journal of Intelligent Systems Technologies and Applications, vol. 19, no. 6, pp. 571–588, 2020.
K. Kowsari, M. Heidarysafa, D.E. Brown, K.J. Meimandi, and L.E. Barnes, “Rmdl: Random multimodal deep learning for classification”, In Proceedings of the 2nd international conference on information system and data mining, pp. 19–28, April, 2018.
M.S. Saccucci, R.W. Amin, and J.M. Lucas, “Exponentially weighted moving average control schemes with variable sampling intervals”, Communications in Statistics-simulation and Computation, vol. 21, no. 3, pp. 627–657, 1992.
I. Naruei, and F. Keynia, “A new optimization method based on COOT bird natural life model”, Expert Systems with Applications, vol. 183, pp. 115352, 2021.
Q. Askari, I. Younas, and M. Saeed, “Political Optimizer: A novel socio-inspired meta-heuristic for global optimization”, Knowledge-based systems, vol. 195, pp. 105709, 2020.
Amazon Reviews for Sentiment Analysis dataset taken from, “https://www.kaggle.com/datasets/bittlingmayer/amazonreviews”, accessed on February, 2023.
J. Khairnar, and M. Kinikar, “Sentiment analysis-based mining and summarizing using SVM-MapReduce”, International Journal of Computer Science and Network Security (IJCSNS), vol. 15, no. 5, pp. 85, 2015.
J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding”, arXiv preprint arXiv:1810.04805, 2018.
H. Yang, B. Zeng, J. Yang, Y. Song, and R. Xu, “A multi-task learning model for chinese-oriented aspect polarity classification and aspect term extraction”, Neurocomputing, vol. 419, pp. 344–356, 2021.
Jia, H., Peng, X. and Lang, C., “Remora optimization algorithm”, Expert Systems with Applications, vol. 185, pp. 115665, 2021.
H. Sharma, G. Hazrati, and J.C. Bansal, “Spider monkey optimization algorithm”, Evolutionary and swarm intelligence algorithms, pp. 43–59, 2019.
S. W. Roberts, “Control Chart Tests Based on Geometric Moving Averages,” Technometrics, Vol. 1, No. 3, pp. 239–250, 1959.
S. W. Roberts, “A Comparison of Some Control Chart Procedures,” Technometrics, Vol. 8, No. 3, pp. 411–430, 1966.
