Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches.

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment expressed from multiple heterogeneous modalities. While transformer-based methods have achieved great success, designing an effective “co-attention” model to associate text modality with nonverbal modalities remains challenging. There are two main problems: 1) the dominant role of the text in modalities is underutilization, and 2) the interaction between modalities is not sufficiently explored. This paper proposes a deep modular Co-Attention Shifting Network (CoASN) for MSA. A Cross-modal Modulation Module based on Co-attention (CMMC) and an Advanced Modality-mixing Adaptation Gate (AMAG) are constructed. The CMMC consists of the Text-guided Co-Attention (TCA) and Interior Transformer Encoder (ITE) units to capture inter-modal features and intra-modal features. With text modality as the core, the CMMC module aims to guide and promote the expression of emotion in nonverbal modalities, and the nonverbal modalities increase the richness of the text-based multimodal sentiment information. In addition, the AMAG module is introduced to explore the dynamical correlations among all modalities. Particularly, this efficient module first captures the nonverbal shifted representations and then combines them to calculate the shifted word embedding representations for the final MSA tasks. Extensive experiments on two commonly used datasets, CMU-MOSI and CMU-MOSEI, demonstrate that our proposed method is superior to the state-of-the-art performance.

Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in Aspect Based Sentiment Analysis. Furthermore, there have been a large number of approaches being proposed to handle this relevant task. However, existing methods for ASTE suffer from powerless interactions between different sources of textual features, and they usually exert an equal impact on each type of feature, which is quite unreasonable while building contextual representation. Therefore, in this paper, we propose a novel Multi-Branch GCN (MBGCN)-based ASTE model to solve this problem. Specifically, our model first generates the enhanced semantic features via the structure-biased BERT, which takes the position of tokens into the transformation of self-attention. Then, a biaffine attention module is utilized to further obtain the specific semantic feature maps. In addition, to enhance the dependency among words in the sentence, four types of linguistic relations are defined, namely part-of-speech combination, syntactic dependency type, tree-based distance, and relative position distance of each word pair, which are further embedded as adjacent matrices. Then, the widely used Graph Convolutional Network (GCN) module is utilized to complete the work of integrating the semantic feature and linguistic feature, which is operated on four types of dependency relations repeatedly. Additionally, an effective refining strategy is employed to detect whether word pairs match or not, which is conducted after the operation of each branch GCN. At last, a shallow interaction layer is designed to achieve the final textual representation by fusing the four branch features with different weights. To validate the effectiveness of MBGCNs, extensive experiments have been conducted on four public and available datasets. Furthermore, the results demonstrate the effectiveness and robustness of MBGCNs, which obviously outperform state-of-the-art approaches.

Aspect-based Sentiment Analysis (ABSA) is a challenging and attractive fine-grained subtask in the Natural Language Processing (NLP) community, which aims to predict the sentiment polarity of each involved aspect term in the given sentence. With the numerous efforts contributed by related researchers, impressive success has been achieved in employing the external knowledge to enhance the sentiment representation. However, in previous works, the utilization of external knowledge is quite straightforward, which doesn't match the sentiment characteristics of human beings. Thus, this work proposes a novel humanized ABSA method, namely Fuzzy Humanized Sentiment-assisted Graph Attention Networks (FHSGAT). Preliminary, to acquire the essential semantic features, the sentence is re-constructed by the manually designed prompt template, which would assist the Pre-trained Language Model (PLM) in generating the target-guided semantic representation. Additionally, based on the normal dependency graph, triple-dimensional external knowledge is injected into the corresponding nodes. Specifically, this paper leverages the Gaussian membership function to fuzzify the sentiment knowledge-enhanced nodes, enabling the networks to obtain the humanized sentiment relationship between graph nodes, namely fuzzy humanized syntactic features. Besides, due to its exquisite performance in information fusion, Graph Attention Networks (GAT) is utilized to accomplish the integration of target-guided semantic representation and fuzzy humanized syntactic features synchronously. Eventually, to validate the effectiveness of the proposed method FHSGAT, extensive experiments are conducted on five publicly and widely used benchmarks, and the corresponding results show the performance of the proposed approach outperforms the state-of-the-art (SOTA) obviously.