Multimodal Sentiment Analysis Research Articles

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism

Multimodal Sentiment Analysis for Movie Scenes Based on a Few-Shot Learning Approach

Multimodal sentiment analysis involves discerning the emotions of speakers through diverse features such as sound, text, and images. However, current research in this field heavily relies on extensive supervised datasets, demanding substantial labor and computational resources. This study introduces a meta-learning-based approach for multimodal sentiment analysis, aiming to delve into emotional information within movie scenes. Leveraging meta-learning techniques, this approach seeks to accurately capture emotions in movie scenes using a limited number of annotated samples, achieving model generalization under constrained labeled data. Specifically, we introduce an optimization-based meta-learning approach for the multimodal sentiment analysis tasks in text and vision, enhancing the model’s ability to generalize to new tasks with limited annotations. Additionally, an innovative strategy based on Meta-Prompting is proposed to handle multimodal data. The merits of the proposed method are validated across different datasets.

Similar modality completion-based multimodal sentiment analysis under uncertain missing modalities

Recently, uncertain missing modalities in multimodal sentiment analysis (MSA) brings a new challenge for sentiment analysis. However, existing research cannot accurately complete the missing modalities, and fail to explore the advantages of the text modality in MSA. For the above problems, this work develops a Similar Modality Completion based-MSA model under uncertain missing modalities (termed as SMCMSA). Firstly, we construct the full modalities samples database (FMSD) by screening out the full modality samples from the whole multimodal dataset, and then predicting and marking the sentiment labels of each modality of the samples with three pre-trained unimodal sentiment analysis model (PTUSA). Next, for completing the uncertain missing modalities, we propose a set of missing modalities completion strategies based on the similar modalities selected from FMSD. For the completed multimodal data, we first encode the text, video and audio modality using the encoder of transformer, then we fuse the representation of text into the representations of video and audio under the guidance of a pre-trained model, thereby improving the quality of video and audio. Finally, we conduct sentiment classification based on the representations of text, video and audio with the softmax function respectively, and get the final decision with the decision-level fusion method. Based on benchmark datasets CMU-MOSI and IEMOCAP, extensive experiments have been conducted to verify that our proposed model SMCMSA has better performance than that of the state-of-the-art baseline models.

Hierarchical denoising representation disentanglement and dual-channel cross-modal-context interaction for multimodal sentiment analysis

Hierarchical denoising representation disentanglement and dual-channel cross-modal-context interaction for multimodal sentiment analysis

A Survey of Cutting-edge Multimodal Sentiment Analysis

The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

Towards bridging the semantic gap between image and text: An empirical approach

Vlogs, Recordings, news, sport coverages are huge sources of multimodal information that do not just limit to text but extend to audio, images and videos. Applications such as summary generation, image/video captioning, multimodal sentiment analysis, cross modal retrieval requires Computer Vision along with Natural Language Processing techniques to extract relevant information. Information from different modalities must be leveraged in order to extract quality content. Hence, reducing the gap between different modalities is of utmost importance. Image to text conversion is an emerging field and employs the use of encoder decoder architecture. Deep CNNs extract the feature of images and sequence to sequence models are used to generate text description. This paper is a contribution towards the growing body of research in multimodal information retrieval. In order to generate the textual description of images, we have performed 5 experiments using the benchmark Flickr8k dataset. In these experiments we have utilized different architectures - simple sequence to sequence model, attention mechanism, transformer-based architecture to name a few. The results have been evaluated using BLEAU score. Results show that the best descriptions are attained by making use of transformer architecture. We have also compared our results with the pretrained visual model vit-gpt2 that incorporates visual transformer.

Text-image semantic relevance identification for aspect-based multimodal sentiment analysis

Aspect-based multimodal sentiment analysis (ABMSA) is an emerging task in the research of multimodal sentiment analysis, which aims to identify the sentiment of each aspect mentioned in multimodal sample. Although recent research on ABMSA has achieved some success, most existing models only adopt attention mechanism to interact aspect with text and image respectively and obtain sentiment output through multimodal concatenation, they often neglect to consider that some samples may not have semantic relevance between text and image. In this article, we propose a Text-Image Semantic Relevance Identification (TISRI) model for ABMSA to address the problem. Specifically, we introduce a multimodal feature relevance identification module to calculate the semantic similarity between text and image, and then construct an image gate to dynamically control the input image information. On this basis, an image auxiliary information is provided to enhance the semantic expression ability of visual feature representation to generate more intuitive image representation. Furthermore, we employ attention mechanism during multimodal feature fusion to obtain the text-aware image representation through text-image interaction to prevent irrelevant image information interfering our model. Experiments demonstrate that TISRI achieves competitive results on two ABMSA Twitter datasets, and then validate the effectiveness of our methods.

A Two-Stage Stacked Transformer Framework for Multimodal Sentiment Analysis

A Two-Stage Stacked Transformer Framework for Multimodal Sentiment Analysis

Transformer-based adaptive contrastive learning for multimodal sentiment analysis

Transformer-based adaptive contrastive learning for multimodal sentiment analysis

An empirical study of Multimodal Entity-Based Sentiment Analysis with ChatGPT: Improving in-context learning via entity-aware contrastive learning

Multimodal Entity-Based Sentiment Analysis (MEBSA) is an emerging task within sentiment analysis, with the objective of simultaneously detecting entity, sentiment, and entity category from multimodal inputs. Despite achieving promising results, most existing MEBSA studies requires a substantial quantity of annotated data. The acquisition of such data is both costly and time-intensive in practical applications. To alleviate the reliance on annotated data, this work explores the potential of in-context learning (ICL) with a representative large language model, ChatGPT, for the MEBSA task. Specifically, we develop a general ICL framework with task instructions for zero-shot learning, followed by extending it to few-shot learning by incorporating a few demonstration samples in the prompt. To enhance the performance of the ICL framework in the few-shot learning setting, we further develop an Entity-Aware Contrastive Learning model to effectively retrieve demonstration samples that are similar to each test sample. Experiments demonstrate that our developed ICL framework exhibits superior performance over other baseline ICL methods, and is comparable to or even outperforms many existing fine-tuned methods on four MEBSA subtasks.

Multi-grained fusion network with self-distillation for aspect-based multimodal sentiment analysis

Aspect-based multimodal sentiment analysis (ABMSA) is an important branch of multimodal sentiment analysis. The goal of ABMSA is to use multimodal information to infer users’ sentiment polarity toward the targeted aspect for supporting corresponding decision-making. The existing ABMSA methods usually focus on exploring aspect-aware fine-grained interactions and demonstrate the benefits of integrating multimodal information. However, such approaches still suffer from the following limitations: (1) coarse-grained semantic information extraction is ignored, (2) the relevance between image and aspect is ignored, and (3) model-agnostic techniques to improve multimodal representations are not employed. To address these limitations, we propose a novel multimodal approach named multi-grained fusion network with self-distillation (MGFN-SD). This approach includes unimodal feature extraction, multi-grained representation learning, and self-distillation-based sentiment prediction. The multi-grained representation learning module extracts fine- and coarse-grained interactions based on the aspect–image relevance computation and the similarity calculation for dynamically filtering potential noise brought by the original image. In the sentiment prediction module, self-distillation is employed to transfer knowledge from both hard and soft labels to supervise the training process of each student classifier for improving the quality of multimodal representations. The experimental results on three benchmark datasets indicate the superiority, rationality, and robustness of MGFN-SD.

TSCL-FHFN: two-stage contrastive learning and feature hierarchical fusion network for multimodal sentiment analysis

TSCL-FHFN: two-stage contrastive learning and feature hierarchical fusion network for multimodal sentiment analysis

Research on emotional analysis and integration of multimodal data report

In recent years, emotional analysis has remained a hot topic, permeating people's daily lives and playing an indispensable role in various environments. Researchers can improve products and services and forecast user behavior using sentiment analysis to understand consumers' emotional inclinations and reactions. This paper's comprehensive review of emotional analysis is conducted through various research data. This study integrates emotional analysis and multimodal data research, elaborating on emotional analysis's characteristics and pros and cons from aspects such as facial expressions, voice information, and textual data. This paper points out the current applications of emotional analysis and recent research advancements, selecting various features and methods for emotion tendency classification, and utilizing existing technological tools for analysis. Lastly, this work combines multimodal data with various studies to make emotional analysis more widely and aptly applied in everyday life. There are greater opportunities for sentiment analysis in various domains thanks to the advancements in multimodal sentiment analysis research and modal fusion technologies.

Sentiment analysis on a low-resource language dataset using multimodal representation learning and cross-lingual transfer learning

Affect Sensing is a rapidly growing field with the potential to revolutionize human–computer interaction, healthcare, and many more applications. Multimodal Sentiment Analysis (MSA) is a recent research area that exploits the multimodal nature of video data for affect sensing. However, the success of a multimodal framework depends on addressing the challenges associated with integrating diverse modalities and selecting informative features. We propose a novel multimodal representation learning framework using multimodal autoencoders that learns a comprehensive representation of the underlying heterogeneous modalities. Affect Sensing is even more challenging in low-resource languages because annotated video datasets and language-specific models are limited. To address this concern, we introduce Multimodal Sentiment Analysis Corpus in Tamil (MSAT), a small-sized dataset in the Tamil language for MSA, and exhibit how a novel technique involving cross-lingual transfer learning in a multimodal setting, leverages the knowledge gained by training the model on a larger English MSA dataset to fine-tune a much smaller Tamil MSA dataset. Our transfer learning model achieves significant gain in the Tamil dataset by a large margin. Our experiments demonstrate that we can build efficient, generalized models for low-resource languages by using the existing MSA datasets.

A face retrieval technique combining large models and artificial neural networks

SummaryMultimodal sentiment analysis is a popular research direction in the field of affective computing. It extends unimodal‐based sentiment analysis to the environment based on multimodal information exchange. Face retrieval is the most critical technology in the current multimodal sentiment analysis field. Traditional methods for face retrieval rely on large amounts of data to train the matching relationship between text and face images. This approach suffers from several issues, including high data acquisition costs, insufficient or poor quality training data, and high computational costs for training models. To address the aforementioned issues, this paper proposes a face retrieval technique that merges a large language model with a visual base model. This technique explores the use of zero samples for training, eliminating the need to collect data and train the model to achieve face retrieval. As a result, it significantly reduces the cost requirements of data and computational power. First, an accurate natural text of face description is generated using a large language model based on cue engineering based on face‐independent features. Second, the text and images are encoded using a visual base model and mapped into the same multidimensional space for matching. The experimental results indicate that this method performs well on the CelebA dataset. Extracting 31 different sets of face feature descriptions from the CelebA dataset for retrieval achieves 72% on top1 accuracy and 93% on top3 accuracy. Compared to traditional methods, the method proposed in this paper achieves considerable face retrieval accuracy without collecting text image pairs of data and without training the model.

A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Missing Modalities

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of modality completeness. However, many inevitable factors in real-world scenarios lead to uncertain missing modalities, which invalidate the fixed multimodal fusion approaches. To this end, we propose a Unified multimodal Missing modality self-Distillation Framework (UMDF) to handle the problem of uncertain missing modalities in MSA. Specifically, a unified self-distillation mechanism in UMDF drives a single network to automatically learn robust inherent representations from the consistent distribution of multimodal data. Moreover, we present a multi-grained crossmodal interaction module to deeply mine the complementary semantics among modalities through coarse- and fine-grained crossmodal attention. Eventually, a dynamic feature integration module is introduced to enhance the beneficial semantics in incomplete modalities while filtering the redundant information therein to obtain a refined and robust multimodal representation. Comprehensive experiments on three datasets demonstrate that our framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.

A Novel Energy Based Model Mechanism for Multi-Modal Aspect-Based Sentiment Analysis

Multi-modal aspect-based sentiment analysis (MABSA) has recently attracted increasing attention. The span-based extraction methods, such as FSUIE, demonstrate strong performance in sentiment analysis due to their joint modeling of input sequences and target labels. However, previous methods still have certain limitations: (i) They ignore the difference in the focus of visual information between different analysis targets (aspect or sentiment). (ii) Combining features from uni-modal encoders directly may not be sufficient to eliminate the modal gap and can cause difficulties in capturing the image-text pairwise relevance. (iii) Existing span-based methods for MABSA ignore the pairwise relevance of target span boundaries. To tackle these limitations, we propose a novel framework called DQPSA. Specifically, our model contains a Prompt as Dual Query (PDQ) module that uses the prompt as both a visual query and a language query to extract prompt-aware visual information and strengthen the pairwise relevance between visual information and the analysis target. Additionally, we introduce an Energy-based Pairwise Expert (EPE) module that models the boundaries pairing of the analysis target from the perspective of an Energy-based Model. This expert predicts aspect or sentiment span based on pairwise stability. Experiments on three widely used benchmarks demonstrate that DQPSA outperforms previous approaches and achieves a new state-of-the-art performance. The code will be released at https://github.com/pengts/DQPSA.

Tri-CLT: Learning Tri-Modal Representations with Contrastive Learning and Transformer for Multimodal Sentiment Recognition

Multimodal Sentiment Analysis (MSA) has become an essential area of research to achieve more accurate sentiment analysis by integrating multiple perceptual modalities such as text, vision, and audio. However, most previous studies failed to align the various modalities well and ignored the differences in semantic information, leading to inefficient fusion between modalities and generating redundant information. In order to solve the above problems, this paper proposes a transformer-based network model, Tri-CLT. Specifically, this paper designs Integrating Fusion Block to fuse modal features to enhance their semantic information and mitigate the secondary complexity of paired sequences in the transformer. Meanwhile, the cross-modal attention mechanism is utilized for complementary learning between modalities to enhance the model performance. In addition, contrastive learning is introduced to improve the model's representation of learning ability. Finally, this paper conducts experiments on CMU-MOSEI aligned and unaligned data, and the experimental results show that the proposed method outperforms the existing methods.

Text-Centric Multimodal Contrastive Learning for Sentiment Analysis

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 feature fusion methods often hinge on spatial consistency assumptions, neglecting essential information about modality interactions and sample relationships within the feature space. In order to surmount these challenges, we propose a text-centric multimodal contrastive learning framework (TCMCL). This framework centers around text and augments text features separately from audio and visual perspectives. In order to effectively learn feature space information from different cross-modal augmented text features, we devised two contrastive learning tasks based on instance prediction and sentiment polarity; this promotes implicit multimodal fusion and obtains more abstract and stable sentiment representations. Our model demonstrates performance that surpasses the current state-of-the-art methods on both the CMU-MOSI and CMU-MOSEI datasets.

Optimized Queuing Modeling in Multimodal Sentiment Analysis: An Examination and Application

This paper introduces an innovative queuing model tailored for multimodal sentiment analysis, designed to swiftly generate a preliminary score by prioritizing influential factors from the most valuable modalities. In a bid to yield reliable results in a compressed timeframe, this model meticulously processes data from the modality possessing the paramount effect factor, concurrently trimming down processing time by omitting data from lesser impactful modalities. This discerning approach allows for a dynamic adjustment of the weights assigned to various modalities based on their respective impact variables, culminating in precise and credible sentiment analysis outcomes. In an era where expediency and accuracy are at a premium, the proposed model stands out by ensuring that the sentiment analysis is not just rapid, but also robust and reliable, reflecting the true emotional tonality with a high degree of reliability. The model’s adaptability to fluctuating modalities, ensures its utility across diverse platforms and applications, making it a valuable tool for contemporary sentiment analysis tasks. Amidst a digital landscape that continually evolves, this queuing model for sentiment analysis exemplifies a significant leap forward, proffering enhanced efficiency, accuracy, and reliability in sentiment analysis endeavors.