Ping Huang
ABSTRACT
Considering the forward and backward dependencies of words in Chinese sentences, this paper combines convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) according to a certain framework to form CNN- BiLSTM model, and uses this model to complete the task of emotional classification of Chinese texts. The model uses CNN to extract sentence features and then uses BiLSTM to capture two-way semantics to realize text sentiment classification. Through verification on Sina Weibo comments data set, CNN-BiLSTM can obtain better accuracy, recall and F1-score compared to CNN, RNN, LSTM and BiLSTM. It shows that CNN-BiLSTM can achieve better performance in the field of Chinese sentiment classification.
- Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014. Sentiment Analysis of Short Informal Text. Journal of Artificial Intelligence Research, 50, (August 2014), 723-762. https://doi.org/10.1613/jair.4272Google Scholar
Cross Ref
- Apoorva Arya, Vishal Shukla, Arvind Negi, and Kapil Gupta. 2020. A Review: Sentiment Analysis and Opinion Mining. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC). University of Delhi, New Delhi, India, Article 32, 6 Pages. http://dx.doi.org/10.2139/ssrn.3602548Google ScholarCross Ref
- Zhang J, and Zong C. 2015. Deep Neural Networks in Machine Translation: An Overview. IEEE Intelligent Systems, 30, 5 (September 2015), 16-25, https://doi.org/10.1109/MIS.2015.69Google ScholarDigital Library
- Ashima Yadav, and Dinesh Kumar Vishwakarma. 2020. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53, (August 2020), 4335–4385. https://link.springer.com/article/10.1007/s10462-019-09794-5Google ScholarDigital Library
- Mohd Usama, Belal Ahmad, Enmin Song, M. Shamim Hossain, Mubarak Alrashoud, and Ghulam Muhammad. 2020. Attention-based sentiment analysis using convolutional and recurrent neural network. Future Generation Computer Systems, 113, (December 2020), 571-578. http://dx.doi.org/10.1016/j.future.2020.07.022Google Scholar
- Cristóbal Colón-Ruiz and Isabel Segura-Bedmar. 2020. Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 110, (August 2020): 103539. https://doi.org/10.1016/j.jbi.2020.103539Google ScholarDigital Library
- Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, and Jian Yang. 2018. Densely Connected Bidirectional LSTM with Applications to Sentence Classification. In CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Hohhot, China, 278-287, https://doi.org/10.1007/978-3-319-99501-4_24Google ScholarDigital Library
- Z. Hameed and B. Garcia-Zapirain. 2020. Sentiment Classification Using a Single-Layered BiLSTM Model. IEEE Access. 8, (April 2020), 73992-74001. http://dx.doi.org/10.1109/ACCESS.2020.2988550Google Scholar
- Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural networks, 61, (January 2015), 85-117. https://doi.org/10.1016/j.neunet.2014.09.003Google ScholarDigital Library
- Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, and Gang Wang. 2018. Recent advances in convolutional neural networks. Pattern Recognition, 77, (May 2018), 354-377. https://doi.org/10.1016/J.PATCOG.2017.10.013Google ScholarDigital Library
- Ruben Zazo, Alicia Lozano-Diez, Javier Gonzalez-Dominguez, Doroteo T. Toledano, and Joaquin Gonzalez-Rodriguez. 2016. Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks. Plos One, 11, 1 (January 2016), e0146917, http://dx.doi.org/10.1371/journal.pone.0146917Google ScholarCross Ref
- Guixian Xu, Yueting Meng, Xiaoyu Qiu, Ziheng Yu, and Xu Wu. 2019. Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access, 7, (April 2019), 51522 - 51532, https://doi.org/10.1109/ACCESS.2019.2909919Google ScholarCross Ref
- Jun Xie, Bo Chen, Xinglong Gu, Fengmei Liang, and Xinying Xu. 2019. Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification. IEEE Access, 7, (December 2019), 180558-180570, http://dx.doi.org/10.1109/ACCESS.2019.2957510Google ScholarCross Ref
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems. ACM, NY, United States, 3111-3119. https://dl.acm.org/doi/10.5555/2999792.2999959Google Scholar
- Zhi-Tong Yang, and Jun Zheng. 2016. Research on Chinese text classification based on Word2vec. In 2nd IEEE International Conference on Computer and Communications (ICCC), IEEE, Chengdu, China, 1166-1170, https://doi.org/10.1109/CompComm.2016.7924888Google Scholar
Index Terms
- Sentiment Analysis of Chinese Text Based on CNN-BiLSTM Serial Hybrid Model
Recommendations
Text Sentiment Analysis based on BERT and Convolutional Neural Networks
NLPIR '21: Proceedings of the 2021 5th International Conference on Natural Language Processing and Information RetrievalThe rapid development of the network has accelerated the speed of information circulation. Analyzing the emotional tendency contained in the network text is very helpful to tap the needs of users. However, most of the existing sentiment classification ...
A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis
AbstractAs the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and ...
A Multi-Layer Neural Network Model Integrating BiLSTM and CNN for Chinese Sentiment Recognition
ICCAI '18: Proceedings of the 2018 International Conference on Computing and Artificial IntelligenceTechnology of artificial intelligent has become research focus. Natural language understanding (NLU) is regarded as core technology of AI. Sentiment recognition is a difficult task in NLU; however it is advantageous to business market and public opinion ...
Comments