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AMSA: Adaptive Multimodal Learning for Sentiment Analysis

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Published:24 February 2023Publication History
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Abstract

Efficient recognition of emotions has attracted extensive research interest, which makes new applications in many fields possible, such as human-computer interaction, disease diagnosis, service robots, and so forth. Although existing work on sentiment analysis relying on sensors or unimodal methods performs well for simple contexts like business recommendation and facial expression recognition, it does far below expectations for complex scenes, such as sarcasm, disdain, and metaphors. In this article, we propose a novel two-stage multimodal learning framework, called AMSA, to adaptively learn correlation and complementarity between modalities for dynamic fusion, achieving more stable and precise sentiment analysis results. Specifically, a multiscale attention model with a slice positioning scheme is proposed to get stable quintuplets of sentiment in images, texts, and speeches in the first stage. Then a Transformer-based self-adaptive network is proposed to assign weights flexibly for multimodal fusion in the second stage and update the parameters of the loss function through compensation iteration. To quickly locate key areas for efficient affective computing, a patch-based selection scheme is proposed to iteratively remove redundant information through a novel loss function before fusion. Extensive experiments have been conducted on both machine weakly labeled and manually annotated datasets of self-made Video-SA, CMU-MOSEI, and CMU-MOSI. The results demonstrate the superiority of our approach through comparison with baselines.

REFERENCES

  1. [1] Ahmad Kashif, Mekhalfi Mohamed Lamine, Conci Nicola, Melgani Farid, and Natale Francesco De. 2018. Ensemble of deep models for event recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 2 (2018), 120.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Chung Jessica Elan and Mustafaraj Eni. 2011. Can collective sentiment expressed on twitter predict political elections? In 25th AAAI Conference on Artificial Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Cui Hang, Mittal Vibhu, and Datar Mayur. 2006. Comparative experiments on sentiment classification for online product reviews. In AAAI, Vol. 6. 30.Google ScholarGoogle Scholar
  4. [4] Mazloom Masoud, Rietveld Robert, Rudinac Stevan, Worring Marcel, and Dolen Willemijn Van. 2016. Multimodal popularity prediction of brand-related social media posts. In Proceedings of the 24th ACM International Conference on Multimedia. 197201.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Zhao Sicheng, Wang Shangfei, Soleymani Mohammad, Joshi Dhiraj, and Ji Qiang. 2019. Affective computing for large-scale heterogeneous multimedia data: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 15, 3s (2019), 132.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Jiang Wentao, Liu Si, Gao Chen, Cao Jie, He Ran, Feng Jiashi, and Yan Shuicheng. 2020. PSGAN: Pose and expression robust spatial-aware GAN for customizable makeup transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 51945202.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Mou Luntian, Zhou Chao, Zhao Pengfei, Nakisa Bahareh, Rastgoo Mohammad Naim, Jain Ramesh, and Gao Wen. 2021. Driver stress detection via multimodal fusion using attention-based CNN-LSTM. Expert Systems with Applications 173 (2021), 114693.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Soleymani Mohammad, Garcia David, Jou Brendan, Schuller Björn, Chang Shih-Fu, and Pantic Maja. 2017. A survey of multimodal sentiment analysis. Image and Vision Computing 65 (2017), 314.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Yadav Ashima and Vishwakarma Dinesh Kumar. 2020. Sentiment analysis using deep learning architectures: A review. Artificial Intelligence Review 53, 6 (2020), 43354385.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Mou Luntian, Zhou Chao, Xie Pengtao, Zhao Pengfei, Jain Ramesh C., Gao Wen, and Yin Baocai. 2021. Isotropic self-supervised learning for driver drowsiness detection with attention-based multimodal fusion. IEEE Transactions on Multimedia (2021).Google ScholarGoogle Scholar
  11. [11] Zhao Sicheng, Gao Yue, Jiang Xiaolei, Yao Hongxun, Chua Tat-Seng, and Sun Xiaoshuai. 2014. Exploring principles-of-art features for image emotion recognition. In Proceedings of the 22nd ACM International Conference on Multimedia. 4756.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Kanakaraj Monisha and Guddeti Ram Mohana Reddy. 2015. Performance analysis of ensemble methods on Twitter sentiment analysis using NLP techniques. In Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC’15). IEEE, 169170.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Yıldırım Ezgi, Çetin Fatih Samet, Eryiğit Gülşen, and Temel Tanel. 2015. The impact of NLP on Turkish sentiment analysis. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 7, 1 (2015), 4351.Google ScholarGoogle Scholar
  14. [14] Wang Shangfei and Ji Qiang. 2015. Video affective content analysis: A survey of state-of-the-art methods. IEEE Transactions on Affective Computing 6, 4 (2015), 410430.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Zhao Sicheng, Yao Hongxun, Gao Yue, Ji Rongrong, and Ding Guiguang. 2016. Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Transactions on Multimedia 19, 3 (2016), 632645.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Shoumy Nusrat J., Ang Li-Minn, Seng Kah Phooi, Rahaman D. M. Motiur, and Zia Tanveer. 2020. Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications 149 (2020), 102447.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Kumar Sudhanshu, Yadava Mahendra, and Roy Partha Pratim. 2019. Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Information Fusion 52 (2019), 4152.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Atrey Pradeep K., Hossain M. Anwar, Saddik Abdulmotaleb El, and Kankanhalli Mohan S.. 2010. Multimodal fusion for multimedia analysis: A survey. Multimedia Systems 16, 6 (2010), 345379.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Pérez-Rosas Verónica, Mihalcea Rada, and Morency Louis-Philippe. 2013. Utterance-level multimodal sentiment analysis. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 973982.Google ScholarGoogle Scholar
  20. [20] Poria Soujanya, Cambria Erik, and Gelbukh Alexander. 2015. Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 25392544.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Liu Ningning, Dellandréa Emmanuel, Chen Liming, Zhu Chao, Zhang Yu, Bichot Charles-Edmond, Bres Stéphane, and Tellez Bruno. 2013. Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme. Computer Vision and Image Understanding 117, 5 (2013), 493512.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Yu Wenmeng, Xu Hua, Meng Fanyang, Zhu Yilin, Ma Yixiao, Wu Jiele, Zou Jiyun, and Yang Kaicheng. 2020. Ch-sims: A Chinese multimodal sentiment analysis dataset with fine-grained annotation of modality. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 37183727.Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Chen Minghai, Wang Sen, Liang Paul Pu, Baltrušaitis Tadas, Zadeh Amir, and Morency Louis-Philippe. 2017. Multimodal sentiment analysis with word-level fusion and reinforcement learning. In Proceedings of the 19th ACM International Conference on Multimodal Interaction. 163171.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Poria Soujanya, Cambria Erik, Hazarika Devamanyu, Mazumder Navonil, Zadeh Amir, and Morency Louis-Philippe. 2017. Multi-level multiple attentions for contextual multimodal sentiment analysis. In 2017 IEEE International Conference on Data Mining (ICDM’17). IEEE, 10331038.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] You Quanzeng, Luo Jiebo, Jin Hailin, and Yang Jianchao. 2016. Cross-modality consistent regression for joint visual-textual sentiment analysis of social multimedia. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 1322.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Zadeh Amir, Liang Paul Pu, Mazumder Navonil, Poria Soujanya, Cambria Erik, and Morency Louis-Philippe. 2018. Memory fusion network for multi-view sequential learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Pandeya Yagya Raj, Bhattarai Bhuwan, and Lee Joonwhoan. 2021. Deep-learning-based multimodal emotion classification for music videos. Sensors 21, 14 (2021), 4927.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Li Linghui, Tang Sheng, Deng Lixi, Zhang Yongdong, and Tian Qi. 2017. Image caption with global-local attention. In 31st AAAI Conference on Artificial Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Wu Jie, Hu Haifeng, and Wu Yi. 2018. Image captioning via semantic guidance attention and consensus selection strategy. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 4 (2018), 119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Xie Hongtao, Fang Shancheng, Zha Zheng-Jun, Yang Yating, Li Yan, and Zhang Yongdong. 2019. Convolutional attention networks for scene text recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 15, 1s (2019), 117.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Poria Soujanya, Cambria Erik, Bajpai Rajiv, and Hussain Amir. 2017. A review of affective computing: From unimodal analysis to multimodal fusion. Inf. Fusion 37 (2017), 98125. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Chen Junjun. 2021. Refining the teacher emotion model: Evidence from a review of literature published between 1985 and 2019. Cambridge Journal of Education 51, 3 (2021), 327357.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Church Kenneth Ward. 2017. Word2Vec. Natural Language Engineering 23, 1 (2017), 155162.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Sindagi Vishwanath A. and Patel Vishal M.. 2018. A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recognition Letters 107 (2018), 316.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Zhao Guozhen, Song Jinjing, Ge Yan, Liu Yongjin, Yao Lin, and Wen Tao. 2016. Advances in emotion recognition based on physiological big data. Journal of Computer Research and Development 53, 1 (2016), 80.Google ScholarGoogle Scholar
  36. [36] ReadFace. 2020. ReadFace webpage on 36Kr. http://36kr.com/p/5038637.html. (2020).Google ScholarGoogle Scholar
  37. [37] Krishnamoorthy Srikumar. 2018. Sentiment analysis of financial news articles using performance indicators. Knowledge and Information Systems 56, 2 (2018), 373394.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. [38] Li Xiaodong, Xie Haoran, Chen Li, Wang Jianping, and Deng Xiaotie. 2014. News impact on stock price return via sentiment analysis. Knowledge-Based Systems 69 (2014), 1423.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. [39] Mao Qianren, Li Jianxin, Wang Senzhang, Zhang Yuanning, Peng Hao, He Min, and Wang Lihong. 2019. Aspect-based sentiment classification with attentive neural turing machines. In IJCAI. 51395145.Google ScholarGoogle Scholar
  40. [40] Rao Yanghui, Lei Jingsheng, Wenyin Liu, Li Qing, and Chen Mingliang. 2014. Building emotional dictionary for sentiment analysis of online news. World Wide Web 17, 4 (2014), 723742.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. [41] Zhang Yuxiang, Fu Jiamei, She Dongyu, Zhang Ying, Wang Senzhang, and Yang Jufeng. 2018. Text emotion distribution learning via multi-task convolutional neural network. In IJCAI. 45954601.Google ScholarGoogle Scholar
  42. [42] Chauhan Dushyant Singh, Dhanush S. R., Ekbal Asif, and Bhattacharyya Pushpak. 2020. Sentiment and emotion help sarcasm? A multi-task learning framework for multi-modal sarcasm, sentiment and emotion analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 43514360.Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Mai Ngoc-Dau, Lee Boon-Giin, and Chung Wan-Young. 2021. Affective computing on machine learning-based emotion recognition using a self-made EEG device. Sensors 21, 15 (2021), 5135. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Ramachandram Dhanesh and Taylor Graham W.. 2017. Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine 34, 6 (2017), 96108.Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Mai Sijie, Hu Haifeng, and Xing Songlong. 2019. Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 481492.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Hazer-Rau Dilana, Meudt Sascha, Daucher Andreas, Spohrs Jennifer, Hoffmann Holger, Schwenker Friedhelm, and Traue Harald C.. 2020. The uulmMAC database-A multimodal affective corpus for affective computing in human-computer interaction. Sensors 20, 8 (2020), 2308.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Xu Haiyang, Zhang Hui, Han Kun, Wang Yun, Peng Yiping, and Li Xiangang. 2019. Learning alignment for multimodal emotion recognition from speech. arXiv preprint arXiv:1909.05645 (2019).Google ScholarGoogle Scholar
  48. [48] Xie Yubo, Li Junze, and Pu Pearl. 2020. Uncertainty and surprisal jointly deliver the punchline: Exploiting incongruity-based features for humor recognition. arXiv preprint arXiv:2012.12007 (2020).Google ScholarGoogle Scholar
  49. [49] Wang Xiangyu and Zong Chengqing. 2021. Distributed representations of emotion categories in emotion space. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 23642375.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Basiri Mohammad Ehsan, Nemati Shahla, Abdar Moloud, Cambria Erik, and Acharya U. Rajendra. 2021. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems 115 (2021), 279294.Google ScholarGoogle Scholar
  51. [51] Chen Yuxiao, Yuan Jianbo, You Quanzeng, and Luo Jiebo. 2018. Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In Proceedings of the 26th ACM International Conference on Multimedia. 117125.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Xu Nan, Mao Wenji, and Chen Guandan. 2019. Multi-interactive memory network for aspect based multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 371378.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Bai Xiao, Wang Xiang, Liu Xianglong, Liu Qiang, Song Jingkuan, Sebe Nicu, and Kim Been. 2021. Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognition 120 (2021), 108102.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. [54] Wang Chen, Wang Xiang, Zhang Jiawei, Zhang Liang, Bai Xiao, Ning Xin, Zhou Jun, and Hancock Edwin. 2022. Uncertainty estimation for stereo matching based on evidential deep learning. Pattern Recognition 124 (2022), 108498.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. [55] Ou Xinyu, Ling Hefei, Yu Han, Li Ping, Zou Fuhao, and Liu Si. 2017. Adult image and video recognition by a deep multicontext network and fine-to-coarse strategy. ACM Transactions on Intelligent Systems and Technology (TIST) 8, 5 (2017), 125.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. [56] Yang Xiaocui, Feng Shi, Wang Daling, and Zhang Yifei. 2020. Image-text multimodal emotion classification via multi-view attentional network. IEEE Transactions on Multimedia 23 (2020), 40144026.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Truong Quoc-Tuan and Lauw Hady W.. 2019. Vistanet: Visual aspect attention network for multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 305312.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. [58] Xu Nan and Mao Wenji. 2017. Multisentinet: A deep semantic network for multimodal sentiment analysis. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 23992402.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Nguyen Tuan-Linh, Kavuri Swathi, and Lee Minho. 2019. A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips. Neural Networks 118 (2019), 208219.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] Naseem Usman, Razzak Imran, Musial Katarzyna, and Imran Muhammad. 2020. Transformer based deep intelligent contextual embedding for twitter sentiment analysis. Future Generation Computer Systems 113 (2020), 5869.Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Li Chaozhuo, Wang Senzhang, Wang Yukun, Yu Philip, Liang Yanbo, Liu Yun, and Li Zhoujun. 2019. Adversarial learning for weakly-supervised social network alignment. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9961003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. [62] Lu Jiasen, Xiong Caiming, Parikh Devi, and Socher Richard. 2017. Knowing when to look: Adaptive attention via a visual sentinel for image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 375383.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Mun Jonghwan, Cho Minsu, and Han Bohyung. 2017. Text-guided attention model for image captioning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Qiao Tingting, Dong Jianfeng, and Xu Duanqing. 2018. Exploring human-like attention supervision in visual question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Sennhauser Luzi and Berwick Robert C.. 2018. Evaluating the ability of LSTMs to learn context-free grammars. arXiv preprint arXiv:1811.02611 (2018).Google ScholarGoogle Scholar
  66. [66] Zadeh Amir and Pu Paul. 2018. Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers).Google ScholarGoogle ScholarCross RefCross Ref
  67. [67] Zadeh Amir, Zellers Rowan, Pincus Eli, and Morency Louis-Philippe. 2016. MOSI: Multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv preprint arXiv:1606.06259 (2016).Google ScholarGoogle Scholar
  68. [68] Hu Dou, Wei Lingwei, and Huai Xiaoyong. 2021. DialogueCRN: Contextual reasoning networks for emotion recognition in conversations. arXiv preprint arXiv:2106.01978 (2021).Google ScholarGoogle Scholar
  69. [69] Li Ruifan, Chen Hao, Feng Fangxiang, Ma Zhanyu, Wang Xiaojie, and Hovy Eduard. 2021. Dual graph convolutional networks for aspect-based sentiment analysis. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 63196329.Google ScholarGoogle ScholarCross RefCross Ref
  70. [70] Palvanov Akmaljon and Cho Young Im. 2019. Visnet: Deep convolutional neural networks for forecasting atmospheric visibility. Sensors 19, 6 (2019), 1343.Google ScholarGoogle ScholarCross RefCross Ref
  71. [71] Zadeh Amir, Chen Minghai, Poria Soujanya, Cambria Erik, and Morency Louis-Philippe. 2017. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250 (2017).Google ScholarGoogle Scholar

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 3s
          June 2023
          270 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3582887
          • Editor:
          • Abdulmotaleb El Saddik
          Issue’s Table of Contents

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          Publication History

          • Published: 24 February 2023
          • Online AM: 1 December 2022
          • Accepted: 20 November 2022
          • Revised: 6 October 2022
          • Received: 17 July 2022
          Published in tomm Volume 19, Issue 3s

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