Relevant bibliographies by topics / Multimodal Sentiment Analysis

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multimodal Sentiment Analysis'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Multimodal Sentiment Analysis"

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Qu, Saiying. "A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis." Journal of Electrical Systems 20, no. 6s (2024): 1575–86. http://dx.doi.org/10.52783/jes.3076.

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A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of Engl
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Kaur, Ramandeep, and Sandeep Kautish. "Multimodal Sentiment Analysis." International Journal of Service Science, Management, Engineering, and Technology 10, no. 2 (2019): 38–58. http://dx.doi.org/10.4018/ijssmet.2019040103.

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Multimodal sentiments have become the challenge for the researchers and are equally sophisticated for an appliance to understand. One of the studies that support MS problems is a MSA, which is the training of emotions, attitude, and opinion from the audiovisual format. This survey article covers the comprehensive overview of the last update in this field. Many recently proposed algorithms and various MSA applications are presented briefly in this survey. The article is categorized according to their contributions in the various MSA techniques. The main purpose of this survey is to provide a fu
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Prathi, Ms S. "Multimodal Sentiment Analysis." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 983–89. https://doi.org/10.61137/ijsret.vol.11.issue2.202.

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Zhu, Linlin, Heli Sun, Qunshu Gao, Yuze Liu, and Liang He. "Aspect Enhancement and Text Simplification in Multimodal Aspect-Based Sentiment Analysis for Multi-Aspect and Multi-Sentiment Scenarios." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1683–91. https://doi.org/10.1609/aaai.v39i2.32161.

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Multimodal Aspect-Based Sentiment Analysis (MABSA) plays a pivotal role in the advancement of sentiment analysis technology. Although current methods strive to integrate multimodal information to enhance the performance of sentiment analysis, they still face two critical challenges when dealing with multi-aspect and multi-sentiment data: i) the importance of aspect terms within multimodal data is often overlooked, and ii) models fail to accurately associate specific aspect terms with corresponding sentiment words in multi-aspect and multi-sentiment sentences. To tackle these problems, we propo
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Zhang, Kang, Yushui Geng, Jing Zhao, Jianxin Liu, and Wenxiao Li. "Sentiment Analysis of Social Media via Multimodal Feature Fusion." Symmetry 12, no. 12 (2020): 2010. http://dx.doi.org/10.3390/sym12122010.

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In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies o
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Zhang, Yifei, Zhiqing Zhang, Shi Feng, and Daling Wang. "Visual Enhancement Capsule Network for Aspect-based Multimodal Sentiment Analysis." Applied Sciences 12, no. 23 (2022): 12146. http://dx.doi.org/10.3390/app122312146.

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Multimodal sentiment analysis, which aims to recognize the emotions expressed in multimodal data, has attracted extensive attention in both academia and industry. However, most of the current studies on user-generated reviews classify the overall sentiments of reviews and hardly consider the aspects of user expression. In addition, user-generated reviews on social media are usually dominated by short texts expressing opinions, sometimes attached with images to complement or enhance the emotion. Based on this observation, we propose a visual enhancement capsule network (VECapsNet) based on mult
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Jiang, Tianyue, Sanhong Deng, Peng Wu, and Haibi Jiang. "Real-Time Human-Music Emotional Interaction Based on Deep Learning and Multimodal Sentiment Analysis." Wireless Communications and Mobile Computing 2023 (April 14, 2023): 1–12. http://dx.doi.org/10.1155/2023/4939048.

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Music, as an integral component of culture, holds a prominent position and is widely accessible. There has been growing interest in studying sentiment represented by music and its emotional effects on its audiences, however, much of the existing literature is subjective and overlooks the impact of music on the real-time expression of emotion. In this article, two labeled datasets for music sentiment classification and multimodal sentiment classification were developed. Deep learning is used to classify music sentiment, while decision-level fusion is used to classify the multimodal sentiment of
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Peng, Heng, Xue Gu, Jian Li, Zhaodan Wang, and Hao Xu. "Text-Centric Multimodal Contrastive Learning for Sentiment Analysis." Electronics 13, no. 6 (2024): 1149. http://dx.doi.org/10.3390/electronics13061149.

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Multimodal sentiment analysis aims to acquire and integrate sentimental cues from different modalities to identify the sentiment expressed in multimodal data. Despite the widespread adoption of pre-trained language models in recent years to enhance model performance, current research in multimodal sentiment analysis still faces several challenges. Firstly, although pre-trained language models have significantly elevated the density and quality of text features, the present models adhere to a balanced design strategy that lacks a concentrated focus on textual content. Secondly, prevalent featur
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Huang, Ju, Wenkang Chen, Fangyi Wang, and Haijun Zhang. "Heterogeneous Hierarchical Fusion Network for Multimodal Sentiment Analysis in Real-World Environments." Electronics 13, no. 20 (2024): 4137. http://dx.doi.org/10.3390/electronics13204137.

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Multimodal sentiment analysis models can determine users’ sentiments by utilizing rich information from various sources (e.g., textual, visual, and audio). However, there are two key challenges when deploying the model in real-world environments: (1) the limitations of relying on the performance of automatic speech recognition (ASR) models can lead to errors in recognizing sentiment words, which may mislead the sentiment analysis of the textual modality, and (2) variations in information density across modalities complicate the development of a high-quality fusion framework. To address these c
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Wang, Peicheng, Shuxian Liu, and Jinyan Chen. "CCDA: A Novel Method to Explore the Cross-Correlation in Dual-Attention for Multimodal Sentiment Analysis." Applied Sciences 14, no. 5 (2024): 1934. http://dx.doi.org/10.3390/app14051934.

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With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: (1) inefficiency when modeling the intramodality and intermodality dynamics and (2) inability to effectively fuse multimodal features. In this
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Dissertations / Theses on the topic "Multimodal Sentiment Analysis"

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Poria, Soujanya. "Novel symbolic and machine-learning approaches for text-based and multimodal sentiment analysis." Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/25396.

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Emotions and sentiments play a crucial role in our everyday lives. They aid decision-making, learning, communication, and situation awareness in human-centric environments. Over the past two decades, researchers in artificial intelligence have been attempting to endow machines with cognitive capabilities to recognize, infer, interpret and express emotions and sentiments. All such efforts can be attributed to affective computing, an interdisciplinary field spanning computer science, psychology, social sciences and cognitive science. Sentiment analysis and emotion recognition has also become a n
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Pérez-Rosas, Verónica. "Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc699996/.

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This research is concerned with the identification of sentiment in multimodal content. This is of particular interest given the increasing presence of subjective multimodal content on the web and other sources, which contains a rich and vast source of people's opinions, feelings, and experiences. Despite the need for tools that can identify opinions in the presence of diverse modalities, most of current methods for sentiment analysis are designed for textual data only, and few attempts have been made to address this problem. The dissertation investigates techniques for augmenting linguistic re
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Poussin, Nadine. "Développement des sentiments au travail : dialogues sur l’efficacité et l’utilité chez des médecins du travail." Thesis, Paris, CNAM, 2014. http://www.theses.fr/2015CNAM0981/document.

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A partir d’une intervention auprès de médecins du travail, cette thèse explore les conditions de développement des sentiments au travail. Elle stabilise une conceptualisation de l’affectivité distinguant affect, émotion et sentiment qui pose des rapports entre l’affect lié aux conflits de l’activité (conflits liés à la conception de l’activité comme triade vivante sujet/objet/autrui et conflits liés aux rapports entre le déjà vécu et le vivant) et les sentiments et émotions qui en sont les instruments de réalisation. Le sentiment est défini comme l’instrument de réalisation de l’affect détaché
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Praboda, Chathurangani Rajapaksha Rajapaksha Waththe Vidanelage. "Clickbait detection using multimodel fusion and transfer learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS025.

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Presque tous les internautes sont susceptibles d'être victimes de clickbait, supposant à tort qu’il s’agit d’informations légitimes. Un type important de clickbait se présente sous la forme de spam et de publicités qui sont utilisés pour rediriger les utilisateurs vers des sites web. Un autre type de "clickbait" est conçu pour faire la une des journaux et rediriger les lecteurs vers leurs sites en ligne, mais ces nouvelles sensationnelles peuvent être trompeuses. Il est difficile de prédire le degré de click-baity d'une nouvelle donnée car les clickbait sont des messages très courts et écrits
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Lerch, Soëlie. "Suggestion de dessins animés par similarité émotionnelle : Approches neuronales multimodales combinant contenus et données physiologiques." Electronic Thesis or Diss., Toulon, 2024. http://www.theses.fr/2024TOUL0005.

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Le cadre général de cette thèse concerne l’étude des sentiments et des émotions afin de mieux comprendre leurs impacts, leurs interactions et ainsi améliorer la communication humain-machine. Un auteur peut véhiculer des sentiments et des émotions dans un message écrit ou au travers d’une vidéo et de ses personnages. Ces émotions et sentiments vont être interprétés par un lecteur ou un spectateur qui vont, eux-mêmes, ressentir des émotions. L’identification de ces émotions est subjective et n’est pas toujours facile. Par exemple, pour un spectateur, a-t-il été surpris ? a-t-il eu peur ? les deu
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Wolters, Mónica Catarina da Providência. "Depressive States Identification on Social Networks using Multimodal Models." Master's thesis, 2018. http://hdl.handle.net/10316/86170.

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Trabalho de Projeto do Mestrado Integrado em Engenharia Biomédica apresentado à Faculdade de Ciências e Tecnologia<br>Depressão é uma doença mental comum por todo o mundo. Esta condição pode causar muito sofrimento ao paciente, e também afetar o trabalho, escola e vida familiar. Em casos extremos, a depressão pode mesmo levar ao suicídio. Em Portugal, estima-se que cerca de 400,000 individuos sofram de depressão por ano. É também a maior causa de suicidio, sendo responsável por 70% dos suicídios.Atualmente, os jovens dependem muito das redes sociais, principalmente os que sofrem com depressão.
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Cavalini, Diandre de Paula. "Image Sentiment Analysis of Social Media Data." Master's thesis, 2021. http://hdl.handle.net/10400.6/11847.

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Often a picture is worth a thousand words, and this is a small statement that represents one of the biggest challenges in the Image Sentiment Analysis area. The main theme of this dissertation is the Image Sentiment Analysis of social media, mainly from Twitter, so that it is identified as situations that represent risks (identification of negative situations) or that become a risk (prediction of negative situations). Despite the diversity of work done in the area of image sentiment analysis, it is still a challenging task. Several factors contribute to the difficulty, both more global fa
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Books on the topic "Multimodal Sentiment Analysis"

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Poria, Soujanya, Amir Hussain, and Erik Cambria. Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4.

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Hussain, Amir, Erik Cambria, and Soujanya Poria. Multimodal Sentiment Analysis. Springer, 2018.

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Hussain, Amir, Erik Cambria, and Soujanya Poria. Multimodal Sentiment Analysis. Springer, 2018.

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Combei, Claudia Roberta, and Valeria Reggi. Appraisal, Sentiment and Emotion Analysis in Political Discourse: A Multimodal, Multi-Method Approach. Taylor & Francis Group, 2023.

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Combei, Claudia Roberta, and Valeria Reggi. Appraisal, Sentiment and Emotion Analysis in Political Discourse: A Multimodal, Multi-Method Approach. Routledge, 2023.

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Book chapters on the topic "Multimodal Sentiment Analysis"

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Introduction and Motivation." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_1.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Background." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_2.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Literature Survey and Datasets." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_3.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Concept Extraction from Natural Text for Concept Level Text Analysis." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_4.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "EmoSenticSpace: Dense Concept-Based Affective Features with Common-Sense Knowledge." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_5.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Sentic Patterns: Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_6.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Combining Textual Clues with Audio-Visual Information for Multimodal Sentiment Analysis." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_7.

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Poria, Soujanya, Amir Hussain, and Erik Cambria. "Conclusion and Future Work." In Multimodal Sentiment Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95020-4_8.

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Xu, Hua. "Multimodal Sentiment Analysis." In Multi-Modal Sentiment Analysis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5776-7_6.

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Abinaya, N., V. S. Harikrishnan, S. Santhiya, A. Sesili, and N. V. Nithya Shree. "Multimodal Sentiment Analysis Applications in Healthcare." In Sentiment Analysis Unveiled. CRC Press, 2025. https://doi.org/10.1201/9781003504832-3.

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Conference papers on the topic "Multimodal Sentiment Analysis"

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Aggrawal, Aditi, and Deepika Varshney. "Multimodal Sentiment Analysis: Perceived vs Induced Sentiments." In 2024 Silicon Valley Cybersecurity Conference (SVCC). IEEE, 2024. http://dx.doi.org/10.1109/svcc61185.2024.10637377.

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Kashyap, Swati, Nithin Linga, Kartikeya Vinay Deepak Jakkinapalli, Revanth Ganta, Eeshaan Timmanapalli, and Yashmit. "Multimodal Sentiment Analysis Using RNN." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688069.

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Zhu, Kang, Xuefei Liu, Heng Xie, et al. "Personality-Guided Multimodal Sentiment Analysis." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022007.

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Zhu, Kang, Cunhang Fan, Jianhua Tao, et al. "Dual-View Multimodal Interaction in Multimodal Sentiment Analysis." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10688078.

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Wang, Zijian. "Deep Learning-Based Multimodal Sentiment Analysis." In International Conference on Data Science and Engineering. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012842200004547.

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Cholke, Puja, Gita Kolate, Babusha Kolhe, Maitrey Katkar, Rohan Khandare, and Om Khandare. "Intelligent Multimodal Form Assistance System." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933168.

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Hua, Yuyao, Ruirui Ji, Sifan Yang, Yi Geng, Wei Gao, and Yun Tan. "Hierarchical Fusion Network for Multimodal Sentiment Analysis." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779845.

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Rayasam, Namitha, VV Sai Sridhar, NS Pushkar, et al. "Multimodal Sentiment Analysis for Interviews and Proctoring." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719163.

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Zhu, Kang, Xuefei Liu, Heng Xie, et al. "Transferring Personality Knowledge to Multimodal Sentiment Analysis." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800671.

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Dhal, Diana, Jasaswi Prasad Mohanty, and Sushri Samita Rout. "A Comprehensive Survey on Multimodal Sentiment Analysis." In 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT). IEEE, 2024. https://doi.org/10.1109/ic-sit63503.2024.10862066.

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