Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of the text. While pre-trained language models (PLMs) have set the state-of-the-art (SOTA) for ATSA, they are resource-intensive due to their large model sizes, restricting their wide applications to resource-constrained scenarios. Conversely, conventional machine learning methods, such as Support Vector Machines (SVMs), offer the benefit of less resource requirement but have lower predictive accuracy. This paper introduces an innovative pipeline, termed SVM-ATSA, which bridges the gap between the accuracy of SVM-based methods and the efficiency of PLM-based methods. To improve the feature expression of SVMs and better adapt to the ATSA task, SVM-ATSA decomposes the learning problem into multiple view subproblems, and dynamically selects as well as constructs features with reinforcement learning. The experimental results demonstrate that SVM-ATSA surpasses SOTA PLM-based methods in predictive accuracy while maintaining a faster inference speed and significantly reducing the number of model parameters.

Multimodal sentiment analysis aims to predict sentiment of language text with the help of other modalities, such as vision and acoustic features. Previous studies focused on learning the joint representation of multiple modalities, ignoring some useful knowledge contained in language modal. In this paper, we try to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. Our method consists of two components: shallow fusion part and aggregation part. For the shallow fusion part, we use crossmodal coattention mechanism to obtain bidirectional context information of each two modals to get the fused shallow representations. For the aggregation part, we design a multitask of sentimental words classification to help and guide the deep fusion of the three modalities and obtain the final sentimental words aware fusion representation. We carry out several experiments on CMU-MOSI, CMU-MOSEI and YouTube datasets. The experimental results show that introducing sentimental words prediction as a multitask can really improve the fusion representation of multiple modalities.

Multimodal sentiment analysis aims to learn a joint representation of multiple features. As demonstrated by previous studies, it is shown that the language modality may contain more semantic information than that of other modalities. Based on this observation, we propose a Multi-perspective Fusion Network(MPFN) focusing on Sense Attentive Language for multimodal sentiment analysis. Different from previous studies, we use the language modality as the main part of the final joint representation, and propose a multi-stage and uni-stage fusion strategy to get the fusion representation of the multiple modalities to assist the final language-dominated multimodal representation. In our model, a Sense-Level Attention Network is proposed to dynamically learn the word representation which is guided by the fusion of the multiple modalities. As in turn, the learned language representation can also help the multi-stage and uni-stage fusion of the different modalities. In this way, the model can jointly learn a well integrated final representation focusing on the language and the interactions between the multiple modalities both on multi-stage and uni-stage. Several experiments are carried on the CMU-MOSI, the CMU-MOSEI and the YouTube public datasets. The experiments show that our model performs better or competitive results compared with the baseline models.