Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMultimodal Sentiment AnalysisSentiment AnalysisSentiment ClassificationSimilar Papers 제목 키워드 기반
Benchmarking Multimodal Sentiment Analysis
We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% ove…
BenchmarkingEmotion RecognitionMultimodal Sentiment AnalysisSentiment AnalysisDependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis
Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and determine their sentiment polarity. However, existing approaches often fall …
Aspect-Based Sentiment AnalysisDependency ParsingImage-text matchingSentiment Analysis+1Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment
Multimodal affective computing, learning to recognize and interpret human affects and subjective information from multiple data sources, is still challenging because: (i) it is hard to extract informative features to rep…
Bridging the Gap for Test-Time Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) is an emerging research topic that aims to understand and recognize human sentiment or emotions through multiple modalities. However, in real-world dynamic scenarios, the distribution …
Multimodal Sentiment AnalysisPseudo LabelSentiment AnalysisTest-time AdaptationTowards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) faces two critical challenges: the lack of interpretability in the decision logic of multimodal fusion and modality imbalance caused by disparities in inter-modal information density. …
DenoisingDimensionality ReductionKolmogorov-Arnold NetworksMultimodal Sentiment Analysis+1