This work proposes a novel and simple sequential learning strategy to train models on videos and texts for multimodal sentiment analysis. To estimate sentiment polarities on unseen out-of-distribution data, we introduce a multimodal model that is trained either in a single source domain or multiple source domains using our learning strategy. This strategy starts with learning domain invariant features from text, followed by learning sparse domain-agnostic features from videos, assisted by the selected features learned in text. Our experimental results demonstrate that our model achieves significantly better performance than the state-of-the-art approaches on average in both single-source and multi-source settings. Our feature selection procedure favors the features that are independent to each other and are strongly correlated with their polarity labels. To facilitate research on this topic, the source code of this work will be publicly available upon acceptance.
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis
Information Networking Institute at Carnegie Mellon University (CMU)（卡内基梅隆大学信息网络研究所）；School of Computer Science and Technology at Tongji University（同济大学计算机科学与技术学院）
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；Academy of Cyber（网络空间研究院）
Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
School of Computer, Electronics and Information（计算机、电子与信息学院）；Guangxi University（广西大学）；Guangxi Key Laboratory of Multimedia Communications（广西多媒体通信与网络技术重点实验室）；School of Sociology and Anthropology（社会学与人类学学院）；Sun Yat-sen University（中山大学）
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Henan University（河南大学）
Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on multimodal fusion strategies, and the deep study of modal representation learning was given less attention. Recently, contrastive learning has been confirmed effective at endowing the learned representation with stronger discriminate ability. Inspired by this, we explore the improvement approaches of modality representation with contrastive learning in thi…
FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
Shanghai University（上海大学）；North China Electric Power University（华北电力大学）；Université de Technologie de Compiègne（法国图卢兹国立理工学院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Macquarie University（麦考瑞大学）
University of Macau（澳门大学）；Shanghai Jiao Tong University（上海交通大学）
Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when partial modalities are missing. Several works have addressed the missing modality problem; but most of them only considered the single modality missing case, and ignored the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalitie…
With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model…
A*STAR AI Initiative, Institute of High Performance Computing（A*STAR人工智能计划、高性能计算研究院）；Adobe Research（奥多比研究院）；University of Michigan（密歇根大学）；Centro de Investigación en Computación, Instituto Politécnico Nacional（国立理工学院计算研究中心）；SUTD（新加坡科技设计大学）
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others. Most of the recent work on multimodal fusion does not guarantee the fidelity of the multimodal representation with respect to the unimodal representations. In this paper, we propose a variational autoencoder-based approach for modality fusion that minimizes information loss between unimodal and multimodal representations. We empirically show that this method outperforms the state-of-the-art methods by a significant margin on several popular datasets.
Multimodal sentiment analysis has recently gained popularity because of its relevance to social media posts, customer service calls and video blogs. In this paper, we address three aspects of multimodal sentiment analysis; 1. Cross modal interaction learning, i.e. how multiple modalities contribute to the sentiment, 2. Learning long-term dependencies in multimodal interactions and 3. Fusion of unimodal and cross modal cues. Out of these three, we find that learning cross modal interactions is beneficial for this problem. We perform experiments on two benchmark datasets, CMU Multimodal Opinion …
University of Stirling（斯特林大学）；Edinburgh Napier University（爱丁堡龙比亚大学）；Nanyang Technological University（南洋理工大学）
Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative solution to computationally understand and harvest sentiments from videos by contextually exploiting audio, visual and textual cues. In this paper, we, firstly, present a first of its kind Persian multimodal dataset comprising more than 800 utterances, as a benchmark resource for re…
Universidade da Beira Interior（贝拉内陆大学）；INESC TEC – Institute for Systems and Computer Engineering, Technology and Science（系统与计算机工程、技术与科学研究所）
Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message p…
Southwest Jiaotong University（西南交通大学）；Microsoft Research Asia（微软亚洲研究院）；Southwestern University of Finance and Economics（西南财经大学）；Ocean University of China（中国海洋大学）；Tencent（腾讯）
Fusion technique is a key research topic in multimodal sentiment analysis. The recent attention-based fusion demonstrates advances over simple operation-based fusion. However, these fusion works adopt single-scale, i.e., token-level or utterance-level, unimodal representation. Such single-scale fusion is suboptimal because that different modality should be aligned with different granularities. This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal s…
Multi-modal sentiment analysis plays an important role for providing better interactive experiences to users. Each modality in multi-modal data can provide different viewpoints or reveal unique aspects of a user's emotional state. In this work, we use text, audio and visual modalities from MOSI dataset and we propose a novel fusion technique using a multi-head attention LSTM network. Finally, we perform a classification task and evaluate its performance.
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a \textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore multiple different variations on the multimodal inputs and outputs of these …
University of São Paulo（圣保罗大学）；Institute of Mathematics and Computer Sciences (ICMC)（数学与计算机科学学院（ICMC））
Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that exploring other modalities (e.g., images) increases sentiment analysis performance. State-of-the-art multimodal models, such as CLIP and VisualBERT, are pre-trained on datasets with the text paired with images. Although the results obtained by these models are promising, pre-training and sentiment analysis fine-tuning tasks of these models are computationally exp…
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Singapore University of Social Sciences, Singapore（新加坡社会科学研究大学）
Huazhong University of Science and Technology（华中科技大学）
Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression. Although there have been many successful attempts to construct multimodal representations for MSA, there are still two challenges to be addressed: 1) A more robust multimodal representation needs to be constructed to bridge the heterogeneity gap and cope with the complex multimodal interactions, and 2) the contextual dynamics must be modeled effectively throughout the information flow. In this work, we propose a…
National University of Singapore（新加坡国立大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality-invariant, w…
MS-Mix: Sentiment-Guided Adaptive Augmentation for Multimodal Sentiment Analysis
Chongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；School of Engineering, Westlake University（西湖大学工学院）
University of Oxford（牛津大学）；Imperial College London（伦敦帝国学院）
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the latent emotional state of the user. Thus, we focus on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions." We demonstrate that our multimodal model combining both text and image features outperforms separate models based…
Hong Kong University of Science and Technology（香港科技大学）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Management University（新加坡管理大学）
Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite…
Iran University of Science and Technology（伊朗科技大学）；Sohar University（苏哈尔大学）；University of Queensland（昆士兰大学）
Multimodal sentiment analysis benefits various applications such as human-computer interaction and recommendation systems. It aims to infer the users' bipolar ideas using visual, textual, and acoustic signals. Although researchers affirm the association between cognitive cues and emotional manifestations, most of the current multimodal approaches in sentiment analysis disregard user-specific aspects. To tackle this issue, we devise a novel method to perform multimodal sentiment prediction using cognitive cues, such as personality. Our framework constructs an adaptive tree by hierarchically div…
National Technical University of Athens（雅典国立技术大学）；Athena Research Center（雅典娜研究中心）
Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose Adapted Multimodal BERT (AMB), a BERT-based architecture for multimodal tasks that uses a combination of adapter modules and intermediate fusion layers. The adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. During the adaptation process the pre-trained language model param…
National Technical University of Athens（雅典国家技术大学）；Institute for Language and Speech Processing, Athena Research Center（雅典娜研究中心语言与语音处理研究所）；Behavioral Signal Technologies（行为信号技术公司）
Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs (early fusion). Models of human perception highlight the importance of top-down fusion, where high-level representations affect the way sensory inputs are perceived, i.e. cognition affects perception. These top-down interactions are not captured in current deep learning models. In this work we propose a neural architecture that captures top-down cross-modal interactions, using a feedback mechanism in the forward pass…
People post their opinions and experiences on social media, yielding rich databases of end-users' sentiments. This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in other domains. First, the domain expert can select an image and a term of interest. Then, the pipeline uses image retrieval to find all images showing similar content and applies aspect-based sentiment analysis to outline users' opinions about the selected term. As part of an interd…
FSRF: Factorization-guided Semantic Recovery for Incomplete Multimodal Sentiment Analysis
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；School of Advanced Manufacturing Engineering, Hefei University（合肥大学先进制造工程学院）；College of Global Talents, BITZH, Beijing Institute of Technology（北京理工大学珠海学院全球人才学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）
