Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.

With the increase of intelligent devices leading to increasing demand for traffic, traffic offloading has become a challenging problem. The space-air-ground integrated network (SAGIN) is a superior network architecture to solve this problem. The existing research on SAGIN traffic offloading only considers the single-layer satellite network in the space network. To further expand the resource pool of traffic offloading in SAGIN, we extend the single-layer satellite network into a double-layer satellite network composed of low-orbit satellites (LEO) and high-orbit satellites (GEO). And re-model a four-layer SAGIN architecture consisting of the ground network, the air network, LEO and GEO. Furthermore, we propose a novel Federated Soft Actor-Critic (FeSAC) traffic offloading method with positive environmental exploration to accommodate this dynamic and complex four-layer SAGIN architecture. The FeSAC method uses federated learning to train traffic offloading nodes and then aggregate the training results to obtain the best traffic offloading strategy. The simulation results show that under the four-layer SAGIN, our proposed method can better adapt to the network environment changes by nodes mobility and is better than the existing traffic offloading methods in throughput, packet loss, and transmission delay.

For the task of conversation emotion recognition, recent works focus on speaker relationship modeling but ignore the role of utterance's emotional tendency.In this paper, we propose a new expression paradigm of sentence-level emotion orientation vector to model the potential correlation of emotions between sentence vectors. Based on it, we design an emotion recognition model, which extracts the sentence-level emotion orientation vectors from the language model and jointly learns from the dialogue sentiment analysis model and extracted sentence-level emotion orientation vectors to identify the speaker's emotional orientation during the conversation. We conduct experiments on two benchmark datasets and compare them with the five baseline models.The experimental results show that our model has better performance on all data sets.