Multimodal Sentiment Analysis Based on Interactive Transformer and Soft Mapping

Implicit emotions are often expressed through implicit and weak clues between modalities due to the lack of explicit emotional feature words, representing a significant challenge for multimodal sentiment analysis. In order to improve implicit emotion recognition, this paper proposes a multimodal sentiment analysis method that integrates KAN and the modal dynamic fusion mechanism. This method first introduces the KAN structure to construct a modal feature encoder to enhance the emotional expression ability of features. Then, the emotional contribution weight of each modality is calculated using the difference between the unimodal and multimodal sentiment scores, and the cross-attention mechanism guided by the main modality is used for feature fusion. Experiments on four datasets, CH-SIMS, CH-SIMSv2, MOSI, and MOSEI, show that the proposed method significantly outperforms the mainstream model in multiple indicators, especially when dealing with samples with implicit or ambiguous emotional expressions. The results verify the effectiveness of enhancing feature encoding capabilities and utilizing modal asymmetry information in implicit sentiment analysis.

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progress in this research field, previous studies still lack adequate approaches for handling inter-modal information consistency and the fusion of different categorical features within a single modality. This study aims to effectively extract sentiment coherence information among video, audio, and text and consequently proposes a multimodal sentiment analysis method named joint chain interactive attention (VAE-JCIA, Video Audio Essay–Joint Chain Interactive Attention). In this approach, a 3D CNN is employed for extracting facial features from video, a Conformer is employed for extracting audio features, and a Funnel-Transformer is employed for extracting text features. Furthermore, the joint attention mechanism is utilized to identify key regions where sentiment information remains consistent across video, audio, and text. This process acquires reinforcing features that encapsulate information regarding consistency among the other two modalities. Inter-modal feature interactions are addressed through chained interactive attention, and multimodal feature fusion is employed to efficiently perform emotion classification. The method is experimentally validated on the CMU-MOSEI dataset and the IEMOCAP dataset. The experimental results demonstrate that the proposed method significantly enhances the performance of the multimodal sentiment analysis model.

The traditional multi-modal sentiment analysis (MSA) method usually considers the multi-modal characteristics to be equally important and ignores the contribution of different modes to the final MSA result. Therefore, an MSA method based on hierarchical adaptive feature fusion network is proposed. Firstly, RoBERTa, ResViT, and LibROSA are used to extract different modal features and construct a layered adaptive multi-modal fusion network. Then, the multi-modal feature extraction module and cross-modal feature interaction module are combined to realize the interactive fusion of information between modes. Finally, an adaptive gating mechanism is introduced to design a global multi-modal feature interaction module to learn the unique features of different modes. The experimental results on three public data sets show that the proposed method can make full use of multi-modal information, outperform other advanced comparison methods, improve the accuracy and robustness of sentiment analysis, and is expected to achieve better results in the field of sentiment analysis.

Aiming at the existing multimodal sentiment analysis approaches, which include inadequate extraction of unimodal features, redundancy of independent modal features, insufficient analysis of semantic correlation between data and insufficient fusion, a Web-Semantic Enhanced Multimodal Sentiment Analysis Using Multilayer Cross-Attention Fusion is proposed. The model utilizes deep learning (including XLNet, ResNeSt, and convolutional neural networks) to extract high-level features from text, audio, and visual modes through self-attention mechanisms, and improves the accuracy of emotion classification through multimodal fusion. The results of experiments demonstrate that the suggested MCFMSA can achieve Acc-2, Acc-3, F1, and MAE values of 89.7%, 85.2%, 89.3%, and 0.466 on the CMU-MOSI dataset, respectively; and on the CMU-MOSEI dataset, Acc-2, Acc-3, F1, and MAE values of 88.7%, 82.5%, 86.5%, and 0.475. All of them are significantly improved compared to several other advanced multimodal sentiment analysis methods, which can enhance the accuracy of sentiment classification.

Multimodal sentiment analysis based on disentangled representation learning and cross-modal-context association mining

Multimodal sentiment analysis refers to the use of computers to analyze and identify the emotions that people want to express through the extracted multimodal sentiment features, and it plays a significant role in human-computer interaction and financial market prediction. Most existing approaches to multimodal sentiment analysis use contextual information for modeling, and while this modeling approach can effectively capture the contextual connections within modalities, the correlations between modalities are often overlooked, and the correlations between modalities are also critical to the final recognition results in multimodal sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis approach based on the universal transformer, a framework that uses the universal transformer to model the connections between multiple modalities while employing effective feature extraction methods to capture the contextual connections of individual modalities. We evaluated our proposed method on two benchmark datasets for multimodal sentiment analysis, CMU-MOSI and CMU-MOSEI, and the results outperformed other methods of the same type.

The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals such as assertiveness, dominance, likability, and sincerity based on the provided audio-visual data. The Cross-Cultural Humor Detection Sub-Challenge (MuSe-Humor) dataset expands upon the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset, focusing on the detection of spontaneous humor in a cross-lingual and cross-cultural setting. The main objective of MuSe 2024 is to unite a broad audience from various research domains, including multimodal sentiment analysis, audio-visual affective computing, continuous signal processing, and natural language processing. By fostering collaboration and exchange among experts in these fields, the MuSe 2024 endeavors to advance the understanding and application of sentiment analysis and affective computing across multiple modalities. This baseline paper provides details on each sub-challenge and its corresponding dataset, extracted features from each data modality, and discusses challenge baselines. For our baseline system, we make use of a range of Transformers and expert-designed features and train Gated Recurrent Unit (GRU)-Recurrent Neural Network (RNN) models on them, resulting in a competitive baseline system. On the unseen test datasets of the respective sub-challenges, it achieves a mean Pearson's Correlation Coefficient (ρ) of 0.3573 for MuSe-Perception and an Area Under the Curve (AUC) value of 0.8682 for MuSe-Humor.

Multimodal Sentiment Analysis (MSA) seeks to utilize various types of signals to infer underlying emotional states. Effective representation learning unifies heterogeneous modalities into emotionally salient features, with recent advances leveraging decoupling and distillation to enhance fusion and sentiment discrimination. However, challenges remain, including modal heterogeneity and insufficient sentiment focus in semantic modeling. To address these issues, we propose Decoupled Global-guided Graph Distillation (DGGD), a global multimodal graph distillation framework based on feature decoupling and emotional attention guidance. DGGD decouples multimodal features into homogeneous and heterogeneous representations. Following this principle, it employs a global-level homogeneous graph distillation module to align modality-invariant features in a structure-aware manner, thereby achieving unified representation across modalities. Meanwhile, to enhance feature discriminability, we introduce a masked emotion-aware decoupling strategy that separates shared and modality-specific information and highlights emotionally salient regions. A heterogeneous graph distillation module further refines sentiment-relevant representations across modalities with selective emotional attention. These components are jointly optimized to balance cross-modal consistency and semantic separation. Consequently, experiments on two benchmark datasets demonstrate DGGD’s strong precision and robustness. Notably, as smart cities increasingly adopt IoT devices for real-time emotion-aware human-computer interaction, DGGD offers a lightweight and modular solution compatible with such IoT-enabled applications.

Multimodal sentiment analysis is an extended approach to traditional language-based sentiment analysis, which uses other relevant modality data. Multimodal sentiment analysis usually applies visual, textual, and acoustic representations for sentiment prediction. Recently, various data fusion methodologies have been proposed for multimodal sentiment analysis. In most cases, textual modality plays a major role, and visual and acoustic modalities are used as auxiliary sources for multimodal sentiment analysis. However, in general multimedia such as video, text transcripts of an individual's speech are not provided. Research on an audio-visual sentiment analysis methodology that does not depend on text modality is essential for multimodal sentiment analysis in real-world industrial applications. Therefore, it is important to improve audio-visual sentiment analysis because it currently exhibits lower performance than multimodal sentiment analysis, including text modality. In this paper, we propose heterogeneous modality transfer learning (HMTL) to utilize the knowledge of aligned text data as a source modality in transfer learning to improve audio-visual sentiment analysis performance. Our approach uses a decoder and adversarial learning techniques to reduce the gap between the source and target modalities in the embedded space for multimodal representation. Our proposed methodology experimentally outperformed recent unimodal and bimodal audio-visual sentiment analysis achievements.

With the rapid development of social networks, more and more people express their emotions and opinions via online videos. However, most of the current research on multimodal sentiment analysis cannot do well with effective emotional fusion in multimodal data. To deal with the problem, we propose a multi-tensor fusion network with cross-modal modeling for multimodal sentiment analysis. In this study, the multimodal feature extraction with cross-modal modeling is utilized to obtain the relationship of emotional information between multiple modalities. Moreover, the multi-tensor fusion network is used to model the interaction of multiple pairs of bimodal and realize the emotional prediction of multimodal features. The proposed approach performs well in regression and different dimensions of classification experiments on the two public datasets CMU-MOSI and CMU-MOSEI.

We present a case study as part of a work-in-progress project about multimodal sentiment analysis on historic German plays, taking Emilia Galotti by G. E. Lessing as our initial use case. We analyze the textual version and an audio version (audio book). We focus on ready-to-use sentiment analysis methods: For the textual component, we implement a naive lexicon-based approach and another approach that enhances the lexicon by means of several NLP methods. For the audio analysis, we use the free version of the Vokaturi tool. We compare the results of all approaches and evaluate them against the annotations of a human expert, which serves as a gold standard. For our use case, we can show that audio and text sentiment analysis behave very differently: textual sentiment analysis tends to predict sentiment as rather negative and audio sentiment as rather positive. Compared to the gold standard, the textual sentiment analysis achieves accuracies of 56% while the accuracy for audio sentiment analysis is only 32%. We discuss possible reasons for these mediocre results and give an outlook on further steps we want to pursue in the context of multimodal sentiment analysis on historic plays.

Integrating color cues to improve multimodal sentiment analysis in social media

Human language is multimodal, including textual, visual and acoustic information. The task of multimodal sentiment analysis is to use human multimodal information for sentiment recognition. Among the three modalities, text contains richer information than other modalities. With the development of pre-trained representation models on text, most of multimodal sentiment analysis methods use text as primary information and the other modalities as supplementary information. The existing methods suffer from the following limitations: 1) inherent heterogeneity of multimodal data, which makes multimodal fusion difficult as different modalities reside in different feature spaces; 2) asynchronism caused by the inconsistent sampling rates of the time series data of different modalities. To alleviate the heterogeneity and asynchronism of multimodal data, we propose HMAI-BERT, a hierarchical multimodal alignment and interaction network-enhanced BERT. In HMAI-BERT, to improve the efficiency of multimodal interaction, we introduce a memory network to align the different multimodal representations before fusion. After multimodal alignment, we propose a modal update method to address the problem of asynchronism, where each modality is reinforced by interacting with other modalities. In addition, we introduce a fusion module to integrate the three reinforced modalities, and a sentiment enhanced memory to enhance multimodal representation. Our experiments on two public datasets show that the proposed HMAI-BERT outperforms the state-of-the-art methods.

Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when fusing multimodal data at each time step in the middle layer. This method puts each modal information in the same position and does not distinguish between strong modal information and weak modal information among multiple modalities. At the same time, this method does not pay attention to the embedding characteristics of multimodal signals across the time dimension. In response to the above problems, this paper proposes a new method and model for processing multimodal signals, which takes into account the delay and hysteresis characteristics of multimodal signals across the time dimension. The purpose is to obtain a multimodal fusion feature emotion analysis representation. We evaluate our method on the multimodal sentiment analysis benchmark dataset CMU Multimodal Opinion Sentiment and Emotion Intensity Corpus (CMU-MOSEI). We compare our proposed method with the state-of-the-art model and show excellent results.

Multimodal sentiment analysis has gained popularity as a research field for its ability to predict users’ emotional tendencies more comprehensively. The data fusion module is a critical component of multimodal sentiment analysis, as it allows for integrating information from multiple modalities. However, it is challenging to combine modalities and remove redundant information effectively. In our research, we address these challenges by proposing a multimodal sentiment analysis model based on supervised contrastive learning, which leads to more effective data representation and richer multimodal features. Specifically, we introduce the MLFC module, which utilizes a convolutional neural network (CNN) and Transformer to solve the redundancy problem of each modal feature and reduce irrelevant information. Moreover, our model employs supervised contrastive learning to enhance its ability to learn standard sentiment features from data. We evaluate our model on three widely-used datasets, namely MVSA-single, MVSA-multiple, and HFM, demonstrating that our model outperforms the state-of-the-art model. Finally, we conduct ablation experiments to validate the efficacy of our proposed method.