Open Access
| Issue |
E3S Web Conf.
Volume 290, 2021
2021 3rd International Conference on Geoscience and Environmental Chemistry (ICGEC 2021)
|
|
|---|---|---|
| Article Number | 02034 | |
| Number of page(s) | 4 | |
| Section | Geological and Hydrological Structure and Environmental Planning | |
| DOI | https://doi.org/10.1051/e3sconf/202129002034 | |
| Published online | 14 July 2021 | |
- Pang B, Lee L, Vaithyanathan S, et al. (2002) Thumbs up: Sentiment Classification using Machine Learning Techniques. Empirical Methods in Natural Language Processing, 79-86. [Google Scholar]
- Yang M, Tu W, Wang J, et al. (2017) Attention Based LSTM for Target Dependent Sentiment Classification. National Conference on Artificial Intelligence, 5013-5014. [Google Scholar]
- Lin Xingmin, Ho Chunheng, Xia Luting, Zhao Ruoyi. (2021) Sentiment analysis of low-carbon travel APP user comments based on deep learning. Sustainable Energy Technologies and Assessments, 44. [Google Scholar]
- Li Lei, Wu Xuhui, Liu Ji. (2021) Sentiment Analysis Model of Bi-LSTM with Key Opinion Target Recognition and Deeper Selfattention. Journal of Chinese Computer Systems, 42(03): 504-509. [Google Scholar]
- Luo Yongjian, Yang Xiaohua, Ouyang Chunping, et al. (2021) Merging Naive Bayes and Causal Rules for Text Sentiment Analysis. Journal of Physics: Conference Series, 1757(1). [Google Scholar]
- S. Kumano, K. Nomura. (2019) Multitask Item Response Models for Response Bias Removal from Affective Ratings. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK, 1-7. [Google Scholar]
- K. Chawla, S. Khosla, N. Chhaya, K. Jaidka. (2019) Pre-trained Affective Word Representations. 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK, 17. [Google Scholar]
- Liu F, Wei F, Yu K, et al. (2017) Sentiment classification of reviews on automobile website by combining word2vec and dependency parsing. Proceedings of the International Conference on Smart Computing and Communication, 206-221. [Google Scholar]
- Mohamad Beigi Omid, Moattar Mohammad H. (2020) Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification. Knowledge-Based Systems, 213. [Google Scholar]
- Felipe Bravo-Marquez, Arun Khanchandani, Bernhard Pfahringer. (2021) Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction. Cognitive Computation (prepublish). [Google Scholar]
- Zhou Shengchen, Qu Wenting, Shi Yingzi, et al. (2013) Summary of Chinese Microblog Sentiment Analysis. Computer Applications and Software, 30(03): 161-164+181. [Google Scholar]
- Zhong Jiawa, Liu Wei, Wang Sili, et al. (2021) text sentiment analysis methods and application were reviewed. The data analysis and knowledge discovery, 1-15. [Google Scholar]
- Song Yan. (2020) Research on Multi-level Fine-grained Attribute Mining of Social Media Text Information. Information Science, 38(11): 98-103. [Google Scholar]
- Fan Hao, Li Pengfei. (2021) Short Text Sentiment Analysis Based on FastText Word Vector and Bidirectional GRU Cyclic Neural Network -- A Case Study of Microblog Comment Text. Information Science, 39(04): 15-22. [Google Scholar]
- Du Yixian, Xu Jiapeng, Zhong Linying, et al. (2021) Multi-dimensional Feature Analysis and Visualization of Online Public Opinion Situation and Sentiment: A Case Study of COVID-19 Epidemics. Journal of Geo-information Science, 23(02): 318-330. [Google Scholar]
- Zhou Ziyu, Liu Fang’ai. (2021) Filter gate network based on multi-head attention for aspect-level sentiment classification. Neurocomputing, 441: 214-225. [CrossRef] [Google Scholar]
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