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Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness. [ABSTRACT FROM AUTHOR]

Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method. [ABSTRACT FROM AUTHOR]

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks. [ABSTRACT FROM AUTHOR]