Explainable artificial intelligence for multimodal sentiment analysis in revitalization project management

Sentiment-aware multimodal pre-training for multimodal sentiment analysis

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

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.

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 .

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%.

AtCAF: Attention-based causality-aware fusion network for multimodal 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.

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.

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 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 is critical in many real-world applications like smart cities, healthcare, and human-computer interaction, where sentiment is conveyed through various modalities, including text, audio, and video. However, existing methods still face several critical challenges in mitigating the impact of random modality loss, particularly in preserving the reliability of emotional patterns. To address these limitations, we introduce UniMSA, a novel framework for multimodal sentiment analysis with missing modalities. It addresses the missing data issue by improving the bipolar emotional uncertainty learning. Our approach enhances the reliability of sentiment analysis by integrating both positive and negative emotional uncertainty estimations to recover emotional patterns in randomly missing modalities. Extensive experiments conducted on large-scale multimodal sentiment datasets demonstrate the effectiveness of UniMSA in comparison to state-of-the-art methods.

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.

Sentiment analysis (SA) has become a key tool in understanding consumer feedback in the automotive industry. However, most existing models are limited to unimodal data and fail to capture fine-grained, aspect-level sentiments from multimodal sources such as text, images, and video. Additionally, privacy concerns related to user-generated content remain under-addressed. This study proposes a novel Multimodal Aspect-Based Sentiment Analysis (MASA) framework that integrates textual, visual, and video data for business decision-making in the automotive sector. The framework includes a BERT-based aspect dictionary for extracting domain-specific features, SCV-YOLOv5 for object segmentation in images and videos, and a GRU model enhanced with the Sinu-Sigmoidal Linear Unit (SSLU) activation function for sentiment classification. A K-Anonymity method augmented by Kendall's Tau and Spearman's Rank Correlation is employed to protect user privacy in sentiment data. The framework was evaluated using the MuSe Car dataset, encompassing over 60 car brands and 10,000 data samples per brand. The proposed model achieved 98.94% classification accuracy, outperforming baseline models such as BiLSTM and CNN in terms of Mean Absolute Error (0.14), RMSE (1.01), and F1-score (98.15%). Privacy-preservation tests also showed superior performance, with a 98% privacy-preserving rate and lower information loss than traditional methods. The results demonstrate that integrating multimodal input with deep learning and privacy-aware techniques significantly enhances the accuracy and reliability of sentiment analysis in automotive business contexts. The framework enables better alignment of consumer feedback with strategic decisions such as product development and targeted marketing.

Integrating color cues to improve multimodal sentiment analysis in social media