This work is licensed under the Creative Commons Attribution 4.0 International License.
Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran, J. (2019). Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion. Information Processing & Management, 56(4), 1245–1259. https://doi.org/10.1016/j.ipm.2019.02.018AbdiA.ShamsuddinS. M.HasanS.PiranJ.(2019).Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion.Information Processing & Management,56(4),1245–1259. https://doi.org/10.1016/j.ipm.2019.02.018Search in Google Scholar
Brousmiche, M., Rouat, J., & Dupont, S. (2022). Multimodal Attentive Fusion Network for audio-visual event recognition. Information Fusion, 85, 52–59. https://doi.org/10.1016/j. inffus.2022.03.001BrousmicheM.RouatJ.DupontS.(2022).Multimodal Attentive Fusion Network for audio-visual event recognition.Information Fusion,85,52–59. https://doi.org/10.1016/j.inffus.2022.03.001Search in Google Scholar
Cai, G. Y., & Xia, B. B. (2015). Convolutional Neural Networks for Multimedia Sentiment Analysis. In Li J.Z., Ji H., Zhao D.Y., & Feng Y.S. (Eds.), Natural Language Processing and Chinese Computing (pp. 159–167). Springer International Publishing. https://doi.org/10.1007/978-3-319-25207-0_14CaiG. Y.XiaB. B.(2015).Convolutional Neural Networks for Multimedia Sentiment Analysis. InLiJ.Z.JiH.ZhaoD.Y.FengY.S.(Eds.),Natural Language Processing and Chinese Computing(pp.159–167).Springer International Publishing. https://doi.org/10.1007/978-3-319-25207-0_14Search in Google Scholar
Cambria, E. (2016). Affective Computing and Sentiment Analysis. IEEE Intelligent Systems, 31(2), 102–107. https://doi.org/10.1109/MIS.2016.31CambriaE.(2016).Affective Computing and Sentiment Analysis.IEEE Intelligent Systems,31(2),102–107. https://doi.org/10.1109/MIS.2016.31Search in Google Scholar
Cambria, E., Howard, N., Hsu, J., & Hussain, A. (2013). Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics. 2013 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI), 108–117. https://doi.org/10.1109/CIHLI.2013.6613272CambriaE.HowardN.HsuJ.HussainA.(2013).Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics.2013 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI),108–117. https://doi.org/10.1109/CIHLI.2013.6613272Search in Google Scholar
Campos, V., Jou, B., & Giró-i-Nieto, X. (2017). From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction. Image and Vision Computing, 65, 15–22. https://doi.org/10.1016/j. imavis.2017.01.011CamposV.JouB.Giró-i-NietoX.(2017).From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction.Image and Vision Computing,65,15–22. https://doi.org/10.1016/j.imavis.2017.01.011Search in Google Scholar
Chen, C., Hong, H. S., Guo, J., & Song, B. (2023). Inter-Intra Modal Representation Augmentation with Trimodal Collaborative Disentanglement Network for Multimodal Sentiment Analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 1–14. https://doi.org/10.1109/TASLP.2023.3263801ChenC.HongH. S.GuoJ.SongB.(2023).Inter-Intra Modal Representation Augmentation with Trimodal Collaborative Disentanglement Network for Multimodal Sentiment Analysis.IEEE/ACM Transactions on Audio, Speech, and Language Processing,1–14. https://doi.org/10.1109/TASLP.2023.3263801Search in Google Scholar
Chen, T., Borth, D., Darrell, T., & Chang, S. F. (2014). DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks (arXiv:1410.8586). arXiv. https://doi.org/10.48550/arXiv.1410.8586ChenT.BorthD.DarrellT.ChangS. F.(2014).DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks(arXiv:1410.8586). arXiv. https://doi.org/10.48550/arXiv.1410.8586Search in Google Scholar
Chen, T., Xu, R. F., He, Y. L., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221–230. https://doi.org/10.1016/j.eswa.2016.10.065ChenT.XuR. F.HeY. L.WangX.(2017).Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN.Expert Systems with Applications,72,221–230. https://doi.org/10.1016/j.eswa.2016.10.065Search in Google Scholar
Deng, J., Dong, W., Socher, R., Li, L. J., Kai Li, & Li F. F. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. https://doi.org/10.1109/CVPR.2009.5206848DengJ.DongW.SocherR.LiL. J.KaiLiLiF. F.(2009).ImageNet: A large-scale hierarchical image database.2009 IEEE Conference on Computer Vision and Pattern Recognition,248–255. https://doi.org/10.1109/CVPR.2009.5206848Search in Google Scholar
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-1423DevlinJ.ChangM.-W.LeeK.ToutanovaK.(2019).BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers),4171–4186. https://doi.org/10.18653/v1/N19-1423Search in Google Scholar
Gaspar, R., Pedro, C., Panagiotopoulos, P., & Seibt, B. (2016). Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Computers in Human Behavior, 56, 179–191. https://doi.org/10.1016/j.chb.2015.11.040GasparR.PedroC.PanagiotopoulosP.SeibtB.(2016).Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events.Computers in Human Behavior,56,179–191. https://doi.org/10.1016/j.chb.2015.11.040Search in Google Scholar
Ghorbanali, A., Sohrabi, M. K., & Yaghmaee, F. (2022). Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks. Information Processing & Management, 59(3), 102929. https://doi.org/10.1016/j.ipm.2022.102929GhorbanaliA.SohrabiM. K.YaghmaeeF.(2022).Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks.Information Processing & Management,59(3),102929. https://doi.org/10.1016/j.ipm.2022.102929Search in Google Scholar
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735HochreiterS.SchmidhuberJ.(1997).Long Short-Term Memory.Neural Computation,9(8),1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735Search in Google Scholar
Hu, A., & Flaxman, S. (2018). Multimodal Sentiment Analysis To Explore the Structure of Emotions. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 350–358. https://doi.org/10.1145/3219819.3219853HuA.FlaxmanS.(2018).Multimodal Sentiment Analysis To Explore the Structure of Emotions.Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,350–358. https://doi.org/10.1145/3219819.3219853Search in Google Scholar
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022). Transfer learning for medical image classification: A literature review. BMC Medical Imaging, 22(1), 69. https://doi.org/10.1186/s12880-022-00793-7KimH. E.Cosa-LinanA.SanthanamN.JannesariM.MarosM. E.GanslandtT.(2022).Transfer learning for medical image classification: A literature review.BMC Medical Imaging,22(1),69. https://doi.org/10.1186/s12880-022-00793-7Search in Google Scholar
Liu, G., & Guo, J. B. (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325–338. https://doi.org/10.1016/j. neucom.2019.01.078LiuG.GuoJ. B.(2019).Bidirectional LSTM with attention mechanism and convolutional layer for text classification.Neurocomputing,337,325–338. https://doi.org/10.1016/j.neucom.2019.01.078Search in Google Scholar
Lu, W., Luo, M. Q., Zhang, Z. Y., Zhang, G. B., Ding, H., Chen, H. H., & Chen, J. P. (2019). Result diversification in image retrieval based on semantic distance. Information Sciences, 502, 59–75. https://doi.org/10.1016/j.ins.2019.06.020LuW.LuoM. Q.ZhangZ. Y.ZhangG. B.DingH.ChenH. H.ChenJ. P.(2019).Result diversification in image retrieval based on semantic distance.Information Sciences,502,59–75. https://doi.org/10.1016/j.ins.2019.06.020Search in Google Scholar
Majumder, N., Hazarika, D., Gelbukh, A., Cambria, E., & Poria, S. (2018). Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowledge-Based Systems, 161, 124–133. https://doi.org/10.1016/j.knosys.2018.07.041MajumderN.HazarikaD.GelbukhA.CambriaE.PoriaS.(2018).Multimodal sentiment analysis using hierarchical fusion with context modeling.Knowledge-Based Systems,161,124–133. https://doi.org/10.1016/j.knosys.2018.07.041Search in Google Scholar
Majumder, N., Poria, S., Peng, H. Y., Chhaya, N., Cambria, E., & Gelbukh, A. (2019). Sentiment and Sarcasm Classification With Multitask Learning. IEEE Intelligent Systems, 34(3), 38–43. https://doi.org/10.1109/MIS.2019.2904691MajumderN.PoriaS.PengH. Y.ChhayaN.CambriaE.GelbukhA.(2019).Sentiment and Sarcasm Classification With Multitask Learning.IEEE Intelligent Systems,34(3),38–43. https://doi.org/10.1109/MIS.2019.2904691Search in Google Scholar
Martínez-Rojas, M., Pardo-Ferreira, M. del C., & Rubio-Romero, J. C. (2018). Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43, 196–208. https://doi.org/10.1016/j. ijinfomgt.2018.07.008Martínez-RojasM.Pardo-FerreiraM. del C.Rubio-RomeroJ. C.(2018).Twitter as a tool for the management and analysis of emergency situations: A systematic literature review.International Journal of Information Management,43,196–208. https://doi.org/10.1016/j.ijinfomgt.2018.07.008Search in Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems, 26. https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923 ce901b-Abstract.htmlMikolovT.SutskeverI.ChenK.CorradoG. S.DeanJ.(2013).Distributed Representations of Words and Phrases and their Compositionality.Advances in Neural Information Processing Systems,26. https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.htmlSearch in Google Scholar
Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633. https://doi.org/10.1016/j.eswa.2012.07.059MoraesR.ValiatiJ. F.Gavião NetoW. P.(2013).Document-level sentiment classification: An empirical comparison between SVM and ANN.Expert Systems with Applications,40(2),621–633. https://doi.org/10.1016/j.eswa.2012.07.059Search in Google Scholar
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. https://doi.org/10.3115/v1/D14-1162PenningtonJ.SocherR.ManningC.(2014).GloVe: Global Vectors for Word Representation.Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),1532–1543. https://doi.org/10.3115/v1/D14-1162Search in Google Scholar
Pérez Rosas, V., Mihalcea, R., & Morency, L. P. (2013). Multimodal Sentiment Analysis of Spanish Online Videos. IEEE Intelligent Systems, 28(3), 38–45. https://doi.org/10.1109/MIS.2013.9Pérez RosasV.MihalceaR.MorencyL. P.(2013).Multimodal Sentiment Analysis of Spanish Online Videos.IEEE Intelligent Systems,28(3),38–45. https://doi.org/10.1109/MIS.2013.9Search in Google Scholar
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98–125. https://doi.org/10.1016/j.inffus.2017.02.003PoriaS.CambriaE.BajpaiR.HussainA.(2017).A review of affective computing: From unimodal analysis to multimodal fusion.Information Fusion,37,98–125. https://doi.org/10.1016/j.inffus.2017.02.003Search in Google Scholar
Poria, S., Cambria, E., Howard, N., Huang, G. B., & Hussain, A. (2016). Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174, 50–59. https://doi.org/10.1016/j.neucom.2015.01.095PoriaS.CambriaE.HowardN.HuangG. B.HussainA.(2016).Fusing audio, visual and textual clues for sentiment analysis from multimodal content.Neurocomputing,174,50–59. https://doi.org/10.1016/j.neucom.2015.01.095Search in Google Scholar
Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H. (2019). Sentiment analysis based on improved pre-trained word embeddings. Expert Systems with Applications, 117, 139–147. https://doi.org/10.1016/j.eswa.2018.08.044RezaeiniaS. M.RahmaniR.GhodsiA.VeisiH.(2019).Sentiment analysis based on improved pre-trained word embeddings.Expert Systems with Applications,117,139–147. https://doi.org/10.1016/j.eswa.2018.08.044Search in Google Scholar
Ruwa, N., Mao, Q. R., Song, H. P., Jia, H. J., & Dong, M. (2019). Triple attention network for sentimental visual question answering. Computer Vision and Image Understanding, 189, 102829. https://doi.org/10.1016/j.cviu.2019.102829RuwaN.MaoQ. R.SongH. P.JiaH. J.DongM.(2019).Triple attention network for sentimental visual question answering.Computer Vision and Image Understanding,189,102829. https://doi.org/10.1016/j.cviu.2019.102829Search in Google Scholar
Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR. https://arxiv.org/abs/1409.1556SimonyanK.ZissermanA.(2015).Very Deep Convolutional Networks for Large-Scale Image Recognition.ICLR. https://arxiv.org/abs/1409.1556Search in Google Scholar
Smith, B. G., Smith, S. B., & Knighton, D. (2018). Social media dialogues in a crisis: A mixed-methods approach to identifying publics on social media. Public Relations Review, 44(4), 562–573. https://doi.org/10.1016/j.pubrev.2018.07.005SmithB. G.SmithS. B.KnightonD.(2018).Social media dialogues in a crisis: A mixed-methods approach to identifying publics on social media.Public Relations Review,44(4),562–573. https://doi.org/10.1016/j.pubrev.2018.07.005Search in Google Scholar
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1631–1642. https://aclanthology.org/D13-1170SocherR.PerelyginA.WuJ.ChuangJ.ManningC. D.NgA.PottsC.(2013).Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank.Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing,1631–1642. https://aclanthology.org/D13-1170Search in Google Scholar
Song, K. K., Yao, T., Ling, Q., & Mei, T. (2018). Boosting image sentiment analysis with visual attention. Neurocomputing, 312, 218–228. https://doi.org/10.1016/j.neucom.2018.05.104SongK. K.YaoT.LingQ.MeiT.(2018).Boosting image sentiment analysis with visual attention.Neurocomputing,312,218–228. https://doi.org/10.1016/j.neucom.2018.05.104Search in Google Scholar
Stappen, L., Schumann, L., Sertolli, B., Baird, A., Weigell, B., Cambria, E., & Schuller, B. W. (2021). MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox. Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge, 75–82. https://doi.org/10.1145/3475957.3484451StappenL.SchumannL.SertolliB.BairdA.WeigellB.CambriaE.SchullerB. W.(2021).MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox.Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge,75–82. https://doi.org/10.1145/3475957.3484451Search in Google Scholar
Stieglitz, S., & Linh, D. X. (2013). Social media and political communication: A social media analytics framework. Social Network Analysis and Mining, 3(4), 1277–1291. https://doi.org/10.1007/s13278-012-0079-3StieglitzS.LinhD. X.(2013).Social media and political communication: A social media analytics framework.Social Network Analysis and Mining,3(4),1277–1291. https://doi.org/10.1007/s13278-012-0079-3Search in Google Scholar
Wang, J., Peng, B., & Zhang, X. J. (2018). Using a stacked residual LSTM model for sentiment intensity prediction. Neurocomputing, 322, 93–101. https://doi.org/10.1016/j.neucom.2018.09.049WangJ.PengB.ZhangX. J.(2018).Using a stacked residual LSTM model for sentiment intensity prediction.Neurocomputing,322,93–101. https://doi.org/10.1016/j.neucom.2018.09.049Search in Google Scholar
Wu, P., Li, X. T., Shen, S., & He, D. Q. (2020). Social media opinion summarization using emotion cognition and convolutional neural networks. International Journal of Information Management, 51, 101978. https://doi.org/10.1016/j.ijinfomgt.2019.07.004WuP.LiX. T.ShenS.HeD. Q.(2020).Social media opinion summarization using emotion cognition and convolutional neural networks.International Journal of Information Management,51,101978. https://doi.org/10.1016/j.ijinfomgt.2019.07.004Search in Google Scholar
Yang, J. F., She, D. Y., Sun, M., Cheng, M. M., Rosin, P. L., & Wang, L. (2018). Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions. IEEE Transactions on Multimedia, 20(9), 2513–2525. https://doi.org/10.1109/TMM.2018.2803520YangJ. F.SheD. Y.SunM.ChengM. M.RosinP. L.WangL.(2018).Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions.IEEE Transactions on Multimedia,20(9),2513–2525. https://doi.org/10.1109/TMM.2018.2803520Search in Google Scholar
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738YoungT.HazarikaD.PoriaS.CambriaE.(2018).Recent Trends in Deep Learning Based Natural Language Processing [Review Article].IEEE Computational Intelligence Magazine,13(3),55–75. https://doi.org/10.1109/MCI.2018.2840738Search in Google Scholar
Yusuf, A. A., Feng, C., & Mao, X.L. (2022). An analysis of graph convolutional networks and recent datasets for visual question answering. Artificial Intelligence Review, 55(8), 6277– 6300. https://doi.org/10.1007/s10462-022-10151-2YusufA. A.FengC.MaoX.L.(2022).An analysis of graph convolutional networks and recent datasets for visual question answering.Artificial Intelligence Review,55(8),6277–6300. https://doi.org/10.1007/s10462-022-10151-2Search in Google Scholar
Zadeh, A., Chen, M. H., Poria, S., Cambria, E., & Morency, L.-P. (2017). Tensor Fusion Network for Multimodal Sentiment Analysis. ArXiv:1707.07250 [Cs]. http://arxiv.org/abs/1707.07250ZadehA.ChenM. H.PoriaS.CambriaE.MorencyL.-P.(2017).Tensor Fusion Network for Multimodal Sentiment Analysis. ArXiv:1707.07250 [Cs]. http://arxiv.org/abs/1707.07250Search in Google Scholar
Zhang, K., Li, Y. Q., Wang, J. Y., Cambria, E., & Li, X. L. (2022). Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge. IEEE Transactions on Circuits and Systems for Video Technology, 32(3), 1034–1047. https://doi.org/10.1109/TCSVT.2021.3072412ZhangK.LiY. Q.WangJ. Y.CambriaE.LiX. L.(2022).Real-Time Video Emotion Recognition Based on Reinforcement Learning and Domain Knowledge.IEEE Transactions on Circuits and Systems for Video Technology,32(3),1034–1047. https://doi.org/10.1109/TCSVT.2021.3072412Search in Google Scholar
Zhang, S. X., Wei, Z. L., Wang, Y., & Liao, T. (2018). Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Generation Computer Systems, 81, 395– 403. https://doi.org/10.1016/j.future.2017.09.048ZhangS. X.WeiZ. L.WangY.LiaoT.(2018).Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary.Future Generation Computer Systems,81,395–403. https://doi.org/10.1016/j.future.2017.09.048Search in Google Scholar
Zhang, W., Wang, M., & Zhu, Y. C. (2020). Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics. International Journal of Information Management, 50, 498–514. https://doi.org/10.1016/j.ijinfomgt.2019.04.001ZhangW.WangM.ZhuY. C.(2020).Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics.International Journal of Information Management,50,498–514. https://doi.org/10.1016/j.ijinfomgt.2019.04.001Search in Google Scholar
Zhao, Z. Y., Zhu, H. Y., Xue, Z. H., Liu, Z., Tian, J., Chua, M. C. H., & Liu, M. F. (2019). An image-text consistency driven multimodal sentiment analysis approach for social media. Information Processing & Management, 56(6), 102097. https://doi.org/10.1016/j.ipm.2019.102097ZhaoZ. Y.ZhuH. Y.XueZ. H.LiuZ.TianJ.ChuaM. C. H.LiuM. F.(2019).An image-text consistency driven multimodal sentiment analysis approach for social media.Information Processing & Management,56(6),102097. https://doi.org/10.1016/j.ipm.2019.102097Search in Google Scholar
Zhou, Y. Q., & Moy, P. (2007). Parsing Framing Processes: The Interplay Between Online Public Opinion and Media Coverage. Journal of Communication, 57(1), 79–98. https://doi.org/10.1111/j.0021-9916.2007.00330.xZhouY. Q.MoyP.(2007).Parsing Framing Processes: The Interplay Between Online Public Opinion and Media Coverage.Journal of Communication,57(1),79–98. https://doi.org/10.1111/j.0021-9916.2007.00330.xSearch in Google Scholar
Zhu, T., Li, L. D., Yang, J. F., Zhao, S. C., Liu, H. T., & Qian, J. S. (2022). Multimodal Sentiment Analysis With Image-Text Interaction Network. IEEE Transactions on Multimedia, 1–1. https://doi.org/10.1109/TMM.2022.3160060ZhuT.LiL. D.YangJ. F.ZhaoS. C.LiuH. T.QianJ. S.(2022).Multimodal Sentiment Analysis With Image-Text Interaction Network.IEEE Transactions on Multimedia,1–1. https://doi.org/10.1109/TMM.2022.3160060Search in Google Scholar