Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning

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.

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 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.

Multi-modal sentiment analysis extends conventional text-based definition of sentiment analysis to a multi-modal setup where multiple relevant modalities are leveraged to perform sentiment analysis. In real applications, however, acquiring annotated multi-modal data is normally labor expensive and time-consuming. In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning. The key idea is to leverage the semi-supervised variational autoencoders to mine more information from unlabeled data for multi-modal sentiment analysis. Specifically, the mined information includes both the independent knowledge within single modality and the interactive knowledge among different modalities. Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification.

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

Inferring Sentiments from Supervised Classification of Text and Speech cues using Fuzzy Rules

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment analysis models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily 1 .

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.

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment expressed from multiple heterogeneous modalities. While transformer-based methods have achieved great success, designing an effective “co-attention” model to associate text modality with nonverbal modalities remains challenging. There are two main problems: 1) the dominant role of the text in modalities is underutilization, and 2) the interaction between modalities is not sufficiently explored. This paper proposes a deep modular Co-Attention Shifting Network (CoASN) for MSA. A Cross-modal Modulation Module based on Co-attention (CMMC) and an Advanced Modality-mixing Adaptation Gate (AMAG) are constructed. The CMMC consists of the Text-guided Co-Attention (TCA) and Interior Transformer Encoder (ITE) units to capture inter-modal features and intra-modal features. With text modality as the core, the CMMC module aims to guide and promote the expression of emotion in nonverbal modalities, and the nonverbal modalities increase the richness of the text-based multimodal sentiment information. In addition, the AMAG module is introduced to explore the dynamical correlations among all modalities. Particularly, this efficient module first captures the nonverbal shifted representations and then combines them to calculate the shifted word embedding representations for the final MSA tasks. Extensive experiments on two commonly used datasets, CMU-MOSI and CMU-MOSEI, demonstrate that our proposed method is superior to the state-of-the-art performance.

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.

Integrating color cues to improve multimodal sentiment analysis in social media

Multimodal sentiment analysis aims to harvest people’s opinions or attitudes from multimedia data through fusion techniques. However, existing fusion methods cannot take advantage of the correlation between multimodal data but introduce interference factors. In this paper, we propose an Interactive Transformer and Soft Mapping based method for multimodal sentiment analysis. In the Interactive Transformer layer, an Interactive Multihead Guided-Attention structure composed of a pair of Multihead Attention modules is first utilized to find the mapping relationship between multimodalities. Then, the obtained results are fed into a Feedforward Neural Network. The Soft Mapping layer consisting of stacking Soft Attention module is finally used to map the results to a higher dimension to realize the fusion of multimodal information. The proposed model can fully consider the relationship between multiple modal pieces of information and provides a new solution to the problem of data interaction in multimodal sentiment analysis. Our model was evaluated on benchmark datasets CMU-MOSEI and MELD, and the accuracy is improved by 5.57% compared with the baseline standard.

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.

Multimodal sentiment analysis (MMSA) is a research method that extracts effective information from heterogeneous modal information. Then, MMSA processes the multimodal data and performs sentiment analysis. Along with big data and machine learning development, multimodal sentiment analysis has become a hot research direction in multimodal learning and natural language processing. Although various feature extraction methods and information fusion methods have been continuously proposed, challenges exist in MMSA research. First, in terms of feature extraction, pre-trained models trained with many data sets can obtain higher quality features, but research on how to use these feature extraction methods to extract the best features is still needed. Currently, the more popular feature fusion methods do not focus on the interaction between multiple modal information and the retention of basic information. To overcome these problems a multimodal sentiment analysis model utilizes text features as core modal features, using video and audio modal features as auxiliary modal features, multimodal feature modality attention mechanism to extract the intrinsic connection between different modalities. The attention mechanism uses the features of video modality and audio modality as the focus and then enhances the text modality with the fusion of video modality and modality. To improve the quality of extracted features, this method chooses the transfer learning training method and uses the pre-trained model for processing. This research uses the CMU-MOSI dataset to test the proposed method. Experimental results show that the performance of the proposed model in emotion score prediction and emotion classification tasks exceeds traditional methods and baseline methods.