Multimodal Sentiment Analysis Research Articles

Retraction Note: ICDN: integrating consistency and difference networks by transformer for multimodal sentiment analysis

Retraction Note: ICDN: integrating consistency and difference networks by transformer for multimodal sentiment analysis

Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality representations with multitask learning and contrastive learning. In addition, our approach obtains dynamic weights by considering the homoscedastic uncertainty of each task in multitask learning. Specially, we design two groups of subtasks, which predict the sentiment polarity of unimodal and bimodal representations, to assist in learning representation through a hard parameter-sharing mechanism in the upstream neural network. A loss weight is learned according to the homoscedastic uncertainty of each task. Moreover, a training strategy based on contrastive learning is designed to balance the inconsistency between training and inference caused by the randomness of the dropout layer. This method minimizes the MSE between two submodels. Experimental results on the MOSI and MOSEI datasets show our method achieves better performance than the current state-of-the-art methods by comprehensively considering the intramodality and intermodality interaction information.

Effects of Physiological Signals in Different Types of Multimodal Sentiment Estimation

Multimodal sentiment analysis has become a focus of research in recent years. However, most studies of multimodal sentiment analysis have considered only signals that are observable by humans, such as linguistic, audio and visual information, whereas the contribution of the multimodal fusion of such signals with unobservable signals, i.e., physiological signals, has not been comprehensively explored. In this study, we aim to investigate effects of physiological signals in multimodal sentiment analysis by evaluating all of the fusion models for different types of sentiment estimation in naturalistic human-agent interaction settings. Our results suggest that physiological features are effective in the unimodal model and that the fusion of linguistic representations with physiological features provides the best results for estimating self-sentiment labels as annotated by the users themselves. In contrast, the tensor fusion of linguistic representations with audiovisual features is effective for estimating sentiment labels as annotated by a third party in regression tasks, which can be derived from the corresponding signals that are observable by humans. A detailed analysis of the self-sentiment estimation results suggests that different modalities play different roles in sentiment estimation, and corresponding implications are discussed.

Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis

The wide application of smart devices enables the availability of multimodal data, which can be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works focus on exploring intra- and inter-modal interactions. However, training a network with cross-modal information (language, audio and visual) is still challenging due to the modality gap. Besides, while learning dynamics within each sample draws great attention, the learning of inter-sample and inter-class relationships is neglected. Moreover, the size of datasets limits the generalization ability of the models. To address the afore-mentioned issues, we propose a novel framework HyCon for hybrid contrastive learning of tri-modal representation. Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning, with which the model can fully explore cross-modal interactions, learn inter-sample and inter-class relationships, and reduce the modality gap. Besides, refinement term and modality margin are introduced to enable a better learning of unimodal pairs. Moreover, we devise pair selection mechanism to identify and assign weights to the informative negative and positive pairs. HyCon can naturally generate many training pairs for better generalization and reduce the negative effect of limited datasets. Extensive experiments demonstrate that our method outperforms baselines on multimodal sentiment analysis and emotion recognition.

Hierarchical Interactive Multimodal Transformer for Aspect-Based Multimodal Sentiment Analysis

Aspect-based multimodal sentiment analysis (ABMSA) aims to determine the sentiment polarities of each aspect or entity mentioned in a multimodal post or review. Previous studies to ABMSA can be summarized into two subtasks: aspect-term based multimodal sentiment classification (ATMSC) and aspect-category based multimodal sentiment classification (ACMSC). However, these existing studies have three shortcomings: (1) ignoring the object-level semantics in images; (2) primarily focusing on aspect-text and aspect-image interactions; (3) failing to consider the semantic gap between text and image representations. To tackle these issues, we propose a general Hierarchical Interactive Multimodal Transformer (HIMT) model for ABMSA. Specifically, we extract salient features with semantic concepts from images via an object detection method, and then propose a hierarchical interaction module to first model the aspect-text and aspect-image interactions, followed by capturing the text-image interactions. Moreover, an auxiliary reconstruction module is devised to largely eliminate the semantic gap between text and image representations. Experimental results show that our HIMT model significantly outperforms the state-of-the-art methods on two benchmarks for ATMSC and one benchmark for ACMSC.

Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.

MTR-SAM: Visual Multimodal Text Recognition and Sentiment Analysis in Public Opinion Analysis on the Internet

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.

Image–Text Multimodal Sentiment Analysis Framework of Assamese News Articles Using Late Fusion

Before the arrival of the web as a corpus, people detected positive and negative news based on the understanding of the textual content from physical newspaper rather than an automatic identification approach from readily available e-newspapers. Thus, the earlier sentiment analysis approach is based on unimodal data, and less effort is paid to the multimodal data. However, the presence of multimodal information helps us to get a clearer understanding of the sentiment. To the best of our knowledge, less work has been introduced on the image–text multimodal sentiment analysis framework of Assamese, a low-resource Indian language mostly spoken in the northeast part of India. We built an Assamese news articles dataset consisting of news text and associated images and one image caption to conduct an experimental study. Focusing on important words and discriminative regions of the images mostly related to sentiment, two individual unimodal such as textual and visual models are proposed. The visual model is developed using an encoder-decoder–based image caption generation system. An image–text multimodal approach is proposed to explore the internal correlation between textual and visual features for joint sentiment classification. Finally, we propose the multimodal sentiment analysis framework, i.e., Textual Visual Multimodal Fusion, by employing a late fusion scheme to merge the three different modalities for the final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance than unimodal features.

Multi-level correlation mining framework with self-supervised label generation for multimodal sentiment analysis

Fusion and co-learning are major challenges in multimodal sentiment analysis. Most existing methods either ignore the basic relationships among modalities or fail to maximize their potential correlations. They also do not leverage the knowledge from resource-rich modalities in the analysis of resource-poor modalities. To address these challenges, we propose a multimodal sentiment analysis method based on multilevel correlation mining and self-supervised multi-task learning. First, we propose a unimodal feature fusion- and linguistics-guided Transformer-based framework, multi-level correlation mining framework, to overcome the difficulty of multimodal information fusion. The module exploits the correlation information between modalities from low to high levels. Second, we divided the multimodal sentiment analysis task into one multimodal task and three unimodal tasks (linguistic, acoustic, and visual tasks), and designed a self-supervised label generation module (SLGM) to generate sentiment labels for unimodal tasks. SLGM-based multi-task learning overcomes the lack of unimodal labels in co-learning. Through extensive experiments on the CMU-MOSI and CMU-MOSEI datasets, we demonstrated the superiority of the proposed multi-level correlation mining framework to state-of-the-art methods.

Attention fusion network for multimodal sentiment analysis

Attention fusion network for multimodal sentiment analysis

Multi-modal sentiment analysis based on graph neural network

Thanks to popularity of social media, people are witnessing the rapid proliferation of posts with various modalities. It is worth noting that these multi-modal expressions share certain characteristics, including the interdependence of objects in the posted images, which is sometimes overlooked in previous researches as they focused on single image-text posts and pay little attention on obtaining the global features. In this paper, a neural network with multiple channels for image-text sentiment detection is proposed. The first step is to encode text and images to capture implicit tendencies. Then the introduction of this model obtains multi-modal expressions by collecting the shared characteristics of the dataset. Finally, the attention mechanism provides reliable predictions of the sentiment tendencies of the given pairs of image-text data. The results of experiments conducted on two publicly available datasets crawled from Twitter prove the reliability of the model on multi-modal sentiment detection, since the model precedes previously proposed models in the main evaluating criteria.

Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method.

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.

Attention-based multimodal sentiment analysis and emotion recognition using deep neural networks

There has been a growing interest in multimodal sentiment analysis and emotion recognition in recent years due to its wide range of practical applications. Multiple modalities allow for the integration of complementary information, improving the accuracy and precision of sentiment and emotion recognition tasks. However, working with multiple modalities presents several challenges, including handling data source heterogeneity, fusing information, aligning and synchronizing modalities, and designing effective feature extraction techniques that capture discriminative information from each modality. This paper introduces a novel framework called “Attention-based Multimodal Sentiment Analysis and Emotion Recognition (AMSAER)” to address these challenges. This framework leverages intra-modality discriminative features and inter-modality correlations in visual, audio, and textual modalities. It incorporates an attention mechanism to facilitate sentiment and emotion classification based on visual, textual, and acoustic inputs by emphasizing relevant aspects of the task. The proposed approach employs separate models for each modality to automatically extract discriminative semantic words, image regions, and audio features. A deep hierarchical model is then developed, incorporating intermediate fusion to learn hierarchical correlations between the modalities at bimodal and trimodal levels. Finally, the framework combines four distinct models through decision-level fusion to enable multimodal sentiment analysis and emotion recognition. The effectiveness of the proposed framework is demonstrated through extensive experiments conducted on the publicly available Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. The results confirm a notable performance improvement compared to state-of-the-art methods, attaining 85% and 93% accuracy for sentiment analysis and emotion classification, respectively. Additionally, when considering class-wise accuracy, the results indicate that the “angry” emotion and “positive” sentiment are classified more effectively than the other emotions and sentiments, achieving 96.80% and 93.14% accuracy, respectively.

Multimodal sentiment analysis for social media contents during public emergencies

Abstract Purpose Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis. Design/methodology/approach We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels. Findings We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies. Research limitations In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information. Practical implications We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies. Originality/value We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.

Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews

An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network to deal with both texts and images on the task of document-level multimodal sentiment analysis. The image caption is introduced as an auxiliary, which is aligned with the image to enhance the semantics delivery. Then, a graph is constructed with the sentences and images generated as nodes. In line with the graph learning, the long-distance dependencies can be captured while the visual noise can be filtered. Specifically, a cross-modal graph convolutional network is built for multimodal information fusion. Extensive experiments are conducted on a multimodal dataset from Yelp. Experimental results reveal that our model obtains a satisfying working performance in DLMSA tasks.

Learning discriminative multi-relation representations for multimodal sentiment analysis

Modality representation learning is a critical issue in multimodal sentiment analysis (MSA). A good sentiment representation should contain as much effective information as possible while being discriminative enough to be better recognized. Previous attention-based MSA methods mainly rely on word-level feature interactions to capture intra-modality and inter-modality relations, which may lead to the loss of essential sentiment information. Furthermore, they primarily focus on information fusion but do not give enough importance to feature discrimination. To address these challenges, we propose a modal-utterance-temporal attention network with multimodal sentiment loss (MUTA-Net) for learning discriminative multi-relation representations, where the modal-utterance-temporal attention (MUTA) and multimodal sentiment loss (MMSL) are the two core units. First, we propose MUTA incorporate utterance-level feature vectors into the interactions of different modalities, which can help extract more useful relationships as the utterance-level vectors may contain sentiment information complementary to word-level vectors. Second, MMSL is designed to achieve a large inter-class distance and a small intra-class feature distance simultaneously in multimodal scenes to enhance the discriminative power of feature representations. Our experiments on four public multimodal datasets show that MUTA-Net outperforms previous baselines significantly.

MSFNet: modality smoothing fusion network for multimodal aspect-based sentiment analysis

Multimodal aspect-based sentiment classification (MABSC) aims to determine the sentiment polarity of a given aspect in a sentence by combining text and image information. Although the text and the corresponding image in a sample are associated with aspect information, their features are represented in distinct semantic spaces, creating a substantial semantic gap. Previous research focused primarily on identifying and fusing aspect-level sentiment expressions of different modalities while ignoring their semantic gap. To this end, we propose a novel aspect-based sentiment analysis model named modality smoothing fusion network (MSFNet). In this model, we process the unimodal aspect-aware features via the feature smoothing strategy to partially bridge modality gap. Then we fuse the smoothed features deeply using the multi-channel attention mechanism, to obtain aspect-level sentiment representation with comprehensive representing capability, thereby improving the performance of sentiment classification. Experiments on two benchmark datasets, Twitter2015 and Twitter2017, demonstrate that our model outperforms the second-best model by 1.96% and 0.19% in terms of Macro-F1, respectively. Additionally, ablation studies provide evidence supporting the efficacy of each of our proposed modules. We release the code at: https://github.com/YunjiaCai/MSFNet.

Real-Time Human-Music Emotional Interaction Based on Deep Learning and Multimodal Sentiment Analysis

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 real-time listeners. We combine sentiment analysis with a conventional online music playback system and propose an innovative human-music emotional interaction system based on multimodal sentiment analysis and deep learning. It has been demonstrated through individual observation and questionnaire studies that the interaction between human and musical sentiments has a positive impact on the negative emotions of listeners.

Multimodal Emotion Analysis Model based on Interactive Attention Mechanism

In traditional multi-modal sentiment analysis, feature fusion is usually achieved by simple splicing, and multi-modal sentiment analysis is only trained as a single task, without considering the contribution of inter-modal information interaction to sentiment analysis and the correlation and constraint relationship between multi-modal and single-modal (text, video and audio) tasks. Therefore, a multi-task model based on interactive attention mechanism is proposed in this paper, which uses inter-modal attention mechanism and single-modal self-attention mechanism to train multi-modal sentiment analysis and single-modal sentiment analysis together, so as to make full use of inter-modal and inter-task information sharing, mutual complement, and reduce noise to improve the overall recognition performance. Experiments show that the proposed model performs well on MOSI and MOSEI common data sets for multimodal sentiment analysis.

BAFN: Bi-Direction Attention Based Fusion Network for Multimodal Sentiment Analysis

Attention-based networks currently identify their effectiveness in multimodal sentiment analysis. However, existing methods ignore the redundancy of auxiliary modalities. More importantly, existing methods only attend to top-down attention (static process) or down-top attention (implicit process), leading to the coarse-grained multimodal sentiment context. In this paper, during the preprocessing period, we first propose the multimodal dynamic enhanced block to capture the intra-modality sentiment context. This can effectively decrease the intra-modality redundancy of auxiliary modalities. Furthermore, the bi-direction attention block is proposed to capture fine-grained multimodal sentiment context via the novel bi-direction multimodal dynamic routing mechanism. Specifically, the bi-direction attention block first highlights the explicit and low-level multimodal sentiment context. Then, the low-level multimodal context is transmitted to a carefully designed bi-direction multimodal dynamic routing procedure. This allows us to dynamically update and investigate high-level and much more fine-grained multimodal sentiment contexts. The experiments demonstrate that our fusion network can achieve state-of-the-art performance. Notably, our model outperforms the best baseline on the metric ‘Acc-7’ with an improvement of 6.9%.