Analysis of User’s Opinion using Deep Neural Network Techniques
Keywords:Aspect-specific, Proximity-weighted convolution neural network, Syntax free, Semantic relatedness
Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network.
S. M. Al-Ghuribi, S. A. Mohd Noah and S. Tiun, "Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews," in IEEE Access, vol. 8, pp. 218592-218613, 2020, doi: 10.1109/ACCESS.2020.3042312.
K. Xu, H. Zhao and T. Liu, "Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification," in IEEE Access, vol. 8, pp. 139346-139355, 2020, doi: 10.1109/ACCESS.2020.3012637.
K. Aurangzeb, N. Ayub and M. Alhussein, "Aspect Based Multi-Labeling Using SVM Based Ensembler," in IEEE Access, vol. 9, pp. 26026-26040, 2021, doi: 10.1109/ACCESS.2021.3055768.
M. Shams, N. Khoshavi and A. Baraani-Dastjerdi, "LISA: Language-Independent Method for Aspect-Based Sentiment Analysis," in IEEE Access, vol. 8, pp. 31034-31044, 2020, doi: 10.1109/ACCESS.2020.2973587.
C. R. Aydin and T. Güngör, "Combination of Recursive and Recurrent Neural Networks for Aspect-Based Sentiment Analysis Using Inter-Aspect Relations," in IEEE Access, vol. 8, pp. 77820-77832, 2020, doi: 10.1109/ACCESS.2020.2990306.
A. Ishaq, S. Asghar and S. A. Gillani, "Aspect-Based Sentiment Analysis Using a Hybridized Approach Based on CNN and GA," in IEEE Access, vol. 8, pp. 135499-135512, 2020, doi: 10.1109/ACCESS.2020.3011802.
Y. Han, M. Liu and W. Jing, "Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer," in IEEE Access, vol. 8, pp. 21314-21325, 2020, doi: 10.1109/ACCESS.2020.2969473.
F. Yin, Y. Wang, J. Liu and L. Lin, "The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation," in IEEE Access, vol. 8, pp. 63359-63367, 2020, doi: 10.1109/ACCESS.2020.2984284.
K. Cheng, Y. Yue and Z. Song, "Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism," in IEEE Access, vol. 8, pp. 16387-16396, 2020, doi: 10.1109/ACCESS.2020.2967103.
K. Abdalgader and A. A. Shibli, "Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method," in IEEE Access, vol. 8, pp. 179955-179969, 2020, doi: 10.1109/ACCESS.2020.3028260.
Jagdale R.S., Shirsat V.S., Deshmukh S.N. (2019)” Sentiment Analysis on Product Reviews Using Machine Learning Techniques,” In: Mallick P., Balas V., Bhoi A., Zobaa A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_61.
F. Iqbal et al., "A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction," in IEEE Access, vol. 7, pp. 14637-14652, 2019, doi: 10.1109/ACCESS.2019.2892852.
Kumar, R., Pannu, H.S. &Malhi, A.K. “Aspect-based sentiment analysis using deep networks and stochastic optimization,” Neural Comput&Applic 32, 3221–3235 (2020). https://doi.org/10.1007/s00521-019-04105-z.
Abdi, A., Shamsuddin, S., Hasan, S., &Piran, J. (2019) “Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion,” Inf. Process. Manag., 56, 1245-1259.
Rezaeinia, S.M., Rahmani, R., Ghodsi, A., &Veisi, H. (2019). “Sentiment analysis based on improved pre-trained word embeddings,” Expert Syst. Appl., 117, 139-147.
E. Zuo, H. Zhao, B. Chen and Q. Chen, "Context-Specific Heterogeneous Graph Convolutional Network for Implicit Sentiment Analysis," in IEEE Access, vol. 8, pp. 37967-37975, 2020, doi: 10.1109/ACCESS.2020.2975244.
Meskele, D., &Frasincar, F. (2019) “ALDONA: a hybrid solution for sentence-level aspect-based sentiment analysis using a lexicalised domain ontology and a neural attention model,” Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing.
Tao, J., Fang, X. “Toward multi-label sentiment analysis: a transfer learning-based approach,” J Big Data 7, 1 (2020). https://doi.org/10.1186/s40537-019-0278-0.
Z. Kastrati, A. S. Imran and A. Kurti, "Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs," in IEEE Access, vol. 8, pp. 106799-106810, 2020, doi: 10.1109/ACCESS.2020.3000739.
Da'u, Aminu, et al. "Recommendation system exploiting aspect-based opinion mining with deep learning method." Information Sciences 512 (2020): 1279-1292.
Wu, Guoqiang et al. “Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification.” Neural networks: the official journal of the International Neural Network Society 122 (2020): 24-39.
Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 1145–1148. DOI: https://doi.org/10.1145/3331184.3331351
How to Cite
Copyright (c) 2021 International Journal of Engineering and Applied Physics
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright on any article in the International Journal of Engineering and Applied Physics is retained by the author(s) under the Creative Commons license, which permits unrestricted use, distribution, and reproduction provided the original work is properly cited.
Authors grant IJEAP a license to publish the article and identify IJEAP as the original publisher.
Authors also grant any third party the right to use, distribute and reproduce the article in any medium, provided the original work is properly cited.