Threat intelligence is crucial for the early detection of network security threats, and named entity recognition (NER) plays a critical role in this process. However, traditional NER models based on sequence tagging primarily focus on word-level information for single-token entities, which leads to the inefficient extraction of multi-token entities in intelligence texts. Moreover, traditional NER models provide only a single semantic representation of intelligence texts, meaning that polysemous entities in intelligence texts cannot be effectively classified. To address these problems, this paper proposes a novel model based on segment-level information extraction and similar semantic space construction (SSNER). First, SSNER retrains the traditional BERT model based on a threat intelligence corpus and modifies BERT’s mask mechanism to extract the segment-level word embedding so that the ability of the SSNER to recognize multi-token entities is enhanced. Second, SSNER designs a similar semantic space construction method, which obtains compound semantic representations with symmetrical properties by filtering out the set of similar words and integrating them using self-attention to generate more accurate labels for the polysemous entities. The experimental results on two datasets, DNRTI and Bridges, indicate that SSNER outperforms both baseline and related models. In particular, SSNER achieves an F1-score of 96.02% on the Bridges dataset, exceeding the previous best model by approximately 1.46%.

Financial text analytics methods are employed to examine social media comments, allowing investors to gain insights and make informed financial decisions. Some emojis within these comments often convey diverse semantics, emotions, or intentions depending on the context. However, traditional financial text analysis methods relying on public annotations struggle to identify implicit expressions, leading to suboptimal performance. To address this challenge, this paper proposes an implicit expression recognition model of emojis in social media comments (IER-SMCEM). Firstly, IER-SMCEM innovative designs a data enhancement method based on the implicit expression of emoji. This method expands the pure text financial sentiment analysis dataset into the implicit expression dataset of emoji by homophonic replacement. Secondly, IER-SMCEM designs a prompt learning template to identify the implicit expression of emoji. Through hand-designed templates, large-scale language models can predict the true meaning that emojis are most likely to express. Finally, IER-SMCEM recovers implicit expression by choosing the predictions of models. Thus, the downstream financial sentiment analysis model can more precisely realize the sentiment recognition of the text with emoji by the recovered text. The experimental results indicate that IER-SMCEM achieves a 98.03% accuracy in semantically recovering implicit expressions within financial texts. In the task of financial sentiment analysis, the sentiment analysis model achieves the highest accuracy of 3.99% after restoring the true implied expression of the texts. Therefore, the model can be effectively applied to sentiment analysis or quantitative analysis.

Automated sentiment analysis of extensive financial review videos provides investors with a precise understanding of market sentiment. However, compared to videos in other domains, financial review videos contain extensive financial jargon and exhibit more nuanced sentiment changes in facial expressions and vocal tones. Most traditional models focus solely on either coarse-grained or fine-grained multimodal sentiment expression. This limitation renders them ineffective in handling videos with subtle sentiment changes, restricting their applicability to analysing sentiment in financial review videos. This paper introduces the Multi-Granularity Multimodal Interaction Network (MGMIN-FSA) for analysing sentiment in financial review videos. First, the Fine-grained Multimodal Interaction Network (FMN) is presented, incorporating Kolmogorov-Arnold Network (KAN) and attention mechanisms to extract crucial phrase-level features across modalities, producing fine-grained sentiment expressions. Additionally, KAN-Attention within FMN improves the model's generalization capability. Second, the Coarse-grained Feature Decoupling Network (CFDN) is introduced, establishing anchors at both modal and sentiment feature levels to improve differentiation between positive and negative samples. Finally, MGMIN-FSA integrates fine- and coarse-grained representations for sentiment analysis using downstream classifiers. MGMIN-FSA obtains the best performance on the FMSA-SC and CH-SIMS datasets among all compared methods. Furthermore, experimental results demonstrate that KAN-Attention can maintain high performance despite insufficient data.