Open Access
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 01007
Number of page(s) 10
Section Computer Science and System Design, Application
Published online 23 June 2022
  1. Nasukawa T, Yi J.Sentiment analysis: Capturing favorability using natural language processing[C]// International Conference on Knowledge Capture. DBLP, 2003. [Google Scholar]
  2. Abdulmohsen Al-Thubaity,Qubayl Alqahtani,Abdulaziz Aljandal. Sentiment lexicon for sentiment analysis of Saudi dialect tweets[J]. Procedia Computer Science,2018,142. [Google Scholar]
  3. Lin J, Zhou Y, Yang A, et al. Building of domain sentiment lexicon based on word2vec[J]. Journal of Shandong University (Engineering Science), 2018. [Google Scholar]
  4. PANG B, LEE L, VAITHYANATHAN S, et al. Thumbs up? sentiment classification using machine learning techniques[C]. Empirical Methods in Natural Language Processing, Philadelphia, July 2002, 2002: 79-86. [Google Scholar]
  5. FAN Z, GUO Y, ZHANG Z H, et al. Sentiment analysis of movie reviews based on dictionary and weak tagging information[J]. Journal of Computer Applications, 2018, 38(11): 3084-3088. [Google Scholar]
  6. Jian Zhang, Shifei Ding, Nan Zhang. An overview on probability undirected graphs and their applications in image processing[J]. Neurocomputing,2018. [Google Scholar]
  7. WAN Q B, DONG F M, SUN S F. Text Classification Method Based on BiLSTM-Attention-CNN HybridNeural Network[J]. Computer Applications and Software, 2020,37(9): 94-98, 201. [Google Scholar]
  8. Usama M, Ahmad B, Yang J, et al. Equipping recur-rent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews[J]. Computer Communications, 2019, 148. [Google Scholar]
  9. Hu J M, FU W L, QIAN W, et al.Research on Pol-icyText Classification Model Based on Topic Model and Attention Mechanism[J]. Information studies: Theory and Application,2021,44(07):159-165. [Google Scholar]
  10. Li S, Pan R, Luo H, et al. Adaptive cross-contextual word embedding for word polysemy with unsupervised topic modeling[J]. Knowledge-Based Systems, 2021, 218(4):106827. [CrossRef] [Google Scholar]
  11. GOODFELLOW I J, SHLENS J, SZEGEDY C.Explaining and harnessing adversarial examples[G]//Proceedings of the International Conference on MachineLearning. Lille, France: International Machine Learning Society, 2015: 1-13. [Google Scholar]
  12. MIYATA T,DAI A M, GOODFELLOW I. Adversarial training methods for semi-supervised text classification[G]//Proceedings of the International Conference on Learning Representations. Toulon, France: International Machine Learning Society, 2017:1-11. [Google Scholar]
  13. Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks[J]. Computer Science, 2013. [Google Scholar]
  14. ZHANG X H.Research on Text Representation and Text Classification Method Based on Adversarial Training [D].Beijing Jiaotong University,2020. [Google Scholar]
  15. BAI J, LI F, JI D H. Attention based BiLSTM-CNNChinese microblogging position detection model[J]. Computer Applications and Software, 2018, 35(3): 266-274. [Google Scholar]
  16. Aishan Wumaier, WEI W L, Zaokere Kaddeer. Sentiment analysis based on bilstm+attention in sports fie-ld[J]. Journal of Xinjiang University(Natural Science Edition in Chinese and English), 2020, 37(2):142-149. [Google Scholar]
  17. LUO F, WANG H F. Chinese text sentiment classification by RNN-CNN[J]. Acta Scientiarum NaturaliumUniversitatis Pekinensis, 2018, 54(3): 459-465. [Google Scholar]
  18. YU W, ZHOU W N. Sentiment analysis of commodity reviews based on LSTM[J]. Computer Systems and Applications, 2018, 27(8): 159-163. [Google Scholar]
  19. REN M, GAN G. Text emotion classification based on bidirectional LSTM model [J]. Computer Engineering and Design, 2018,39(07):2064-2068. [Google Scholar]
  20. WANG L Y, LIU C H*, CAI D B, et al. Text Sentiment Analysis Based on CNN-BiLSTM Network and attention Model[J]. Journal of Wuhan Institute of Technology,2019,(04):386-391. [Google Scholar]
  21. Peng C, Sun Z, Bing L, et al. Recurrent Attention Network on Memory for Aspect Sentiment Analysis[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. [Google Scholar]

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