Uneingeschränkter Zugang

Multimodal sentiment analysis for social media contents during public emergencies


Zitieren

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.2019.02.018 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.2019.02.018 Search 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.2022.03.001 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.2022.03.001 Search 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_14 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_14 Search in Google Scholar

Cambria, E. (2016). Affective Computing and Sentiment Analysis. IEEE Intelligent Systems, 31(2), 102–107. https://doi.org/10.1109/MIS.2016.31 Cambria E. ( 2016 ). Affective Computing and Sentiment Analysis . IEEE Intelligent Systems , 31 ( 2 ), 102 107 . https://doi.org/10.1109/MIS.2016.31 Search 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.2013.6613272 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.2013.6613272 Search 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.2017.01.011 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.2017.01.011 Search 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.2023.3263801 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.2023.3263801 Search 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.1410.8586 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.1410.8586 Search 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.2016.10.065 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.2016.10.065 Search 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.2009.5206848 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.2009.5206848 Search 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/N19-1423 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/N19-1423 Search 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.2015.11.040 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.2015.11.040 Search 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.2022.102929 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.2022.102929 Search in Google Scholar

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 Hochreiter S. Schmidhuber J. ( 1997 ). Long Short-Term Memory . Neural Computation , 9 ( 8 ), 1735 1780 . https://doi.org/10.1162/neco.1997.9.8.1735 Search 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/10.1145/3219819.3219853 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/10.1145/3219819.3219853 Search 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/s12880-022-00793-7 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/s12880-022-00793-7 Search 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.2019.01.078 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.2019.01.078 Search 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.2019.06.020 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.2019.06.020 Search 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.2018.07.041 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.2018.07.041 Search 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.2019.2904691 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.2019.2904691 Search 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.2018.07.008 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.2018.07.008 Search 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.html 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/9aa42b31882ec039965f3c4923ce901b-Abstract.html Search 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.2012.07.059 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.2012.07.059 Search 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/D14-1162 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/D14-1162 Search 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.2013.9 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.2013.9 Search 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.2017.02.003 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.2017.02.003 Search 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.2015.01.095 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.2015.01.095 Search 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.2018.08.044 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.2018.08.044 Search 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.2019.102829 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.2019.102829 Search in Google Scholar

Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. ICLR. https://arxiv.org/abs/1409.1556 Simonyan K. Zisserman A. ( 2015 ). Very Deep Convolutional Networks for Large-Scale Image Recognition . ICLR . https://arxiv.org/abs/1409.1556 Search 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.2018.07.005 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.2018.07.005 Search 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/D13-1170 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/D13-1170 Search 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.2018.05.104 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.2018.05.104 Search 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/10.1145/3475957.3484451 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/10.1145/3475957.3484451 Search 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/s13278-012-0079-3 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/s13278-012-0079-3 Search 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.2018.09.049 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.2018.09.049 Search 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.2019.07.004 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.2019.07.004 Search 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.2018.2803520 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.2018.2803520 Search 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.2018.2840738 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.2018.2840738 Search 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/s10462-022-10151-2 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/s10462-022-10151-2 Search 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/1707.07250 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/1707.07250 Search 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.2021.3072412 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.2021.3072412 Search 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.2017.09.048 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.2017.09.048 Search 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.2019.04.001 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.2019.04.001 Search 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.2019.102097 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.2019.102097 Search 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.x 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.x Search 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.2022.3160060 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.2022.3160060 Search in Google Scholar

eISSN:
2543-683X
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Informatik, Informationstechnik, Projektmanagement, Datanbanken und Data Mining