Computer Science ›› 2023, Vol. 50 ›› Issue (3): 298-306.doi: 10.11896/jsjkx.220100156
• Artificial Intelligence • Previous Articles Next Articles
CHEN Zhen1, PU Yuanyuan1,2, ZHAO Zhengpeng1, XU Dan1, QIAN Wenhua1
CLC Number:
[1]KAGAN V,STEVENS A,SUBRAHMANIAN V S.UsingTwitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election [J].IEEE Intelligent Systems,2015,30(1):2-5. [2]BOLLEN J,MAO H N,ZENG S J.Twitter mood predicts the stock market [J].Journal of Computational Science,2011,2(1):1-8. [3]LI X D,XIE H R,CHEN L,et al.News impact on stock price return via sentiment analysis [J].Knowledge-Based Systems,2014,69(15):14-23. [4]HUR M,KANG P,CHO S.Box-office forecasting based on sentiments of movie reviews and Independent subspace method [J].Information Sciences,2016:608-624. [5]XU N,MAO W J.MultiSentiNet:A Deep Semantic Network for Multimodal Sentiment Analysis[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:2399-2402. [6]HUANG F R,ZHANG X M,ZHAO Z H,et al.Image-text sentiment analysis via deep multimodal attentive fusion [J].Know-ledge-Based Systems,2019:167:26-37. [7]LIN M H,MENG Z Q.Multimodal Sentiment Analysis Based on Attention Neural Network [J].Computer Science,2020,47(S2):508-514,548. [8]XU N,MAO W J,CHEN G D.A co-memory network for multimodal sentiment analysis[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.2018:929-932. [9]XU J,HUANG F R,ZHANG X M,et al.Visual-textual sentiment classification with bi-directional multi-level attention networks [J].Knowledge Based Systems,2019,178(AUG.15):61-73. [10]YANG X C,FENG S,WAND D L,et al.Image-Text Multimodal Emotion Classification via Multi-View Attentional Network [J].IEEE Transactions on Multimedia,2021,23(1):4014-4026. [11]ANDERSON P,HE X D,BUEHLER C,et al.Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6077-6086. [12]JIANG H,MISRA I,ROHRBACH M,et al.In defense of grid features for visual question answering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:10264-10273. [13]WEN Z,PENG Y.Multi-level knowledge injecting for visualcommonsense reasoning [J].IEEE Transactions on Circuits and Systems for Video Technology,2020,31(3):1042-1054. [14]ENGIN D,SCHNITZLER F,DUONG N Q K,et al.On the hidden treasure of dialog in video question answering[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:2064-2073. [15]MIKOLOV T,CORRADO G,KAI C,et al.Efficient Estimation of Word Representations in Vector Space [J].Advances in Neural Information Processing Systems,2013,26(1):3111-3119. [16]PENNINGTON J,SOCHER R,MANNING C D.Glove:Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing.EMNLP,2014:1532-1543. [17]LIU Z,LIN Y T,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.IEEE,2021:10012-10022. [18]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:770-778. [19]ZHAND X D,GAO X B,LU W,et al.A Gated Peripheral-Fo-veal Convolutional Neural Network for Unified Image Aesthetic Prediction [J].IEEE Transactions on Multimedia,2019,21(11):2815-2826. [20]MNIH V,HEESS N,GRAVES A,et al.Recurrent models ofvisual attention[C]//Proceedings of the Neural Information Processing Systems.2014:2204-2212. [21]SUN Y,WANG S,LI Y,et al.ERNIE 2.0:A Continual Pre-Training Framework for Language Understanding[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:8968-8975. [22]HU J,SHEN L,ALBANIE S,et al.Squeeze-and-Excitation Networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8) 2011-2023. [23]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling [J].arXiv::1412.3555,2014. [24]ZHAO L,SHANG M,GAO F,et al.Representation learning of image composition for aesthetic prediction [J].Computer Vision and Image Understanding,2020,199(9):103024. [25]ZADEH A,CHEN M,PORIA S,et al.Tensor Fusion Network for Multimodal Sentiment Analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.Copenhagen Denmark,2017:1103-1114. [26]TENG N,ZHU S,LEI P,et al.Sentiment analysis on multi-view social data[C]//International Conference on Multimedia Mode-ling.2016:15-27. [27]MACHAJDIK J,HANBURY A.Affective image classificationusing features inspired by psychology and art theory[C]//Proceedings of the 18th ACM International Conference on Multimedia.New York,NY,USA,2010:83-92. [28]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Net-works for Large-Scale Image Recognition [J].arXiv:1409.1556,2014. [29]SONG K K,YAO T,LING Q,et al.Boosting Image Sentiment Analysis with Visual Attention [J].Neurocomputing,2018,312(27):218-228. [30]CAI G Y,CHU Y Y.Visual SentimentAnalysis Based on Multi-level Features Fusion of Dual Attention [J].Computer Engineering,2021,47(9):227-234. [31]HU A,FLAXMAN S.Multimodal Sentiment Analysis To Explore the Structure of Emotions[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Disco-very & Data Mining.2018:350-358. [32]GUO K X,ZHANG Y X.Visual-textual sentiment analysis method with multi-level spatial attention [J].Journal of Computer Applications,2021,41(10):2835-2841. |
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