With the continuous expansion of the darknet and the increase in various criminal activities in the darknet, darknet traffic identification has become increasingly essential. However, existing darknet traffic identification methods rely on all traffic characteristics, which require a long computing time and a large amount of system resources, resulting in low identification efficiency. To this end, this paper proposes an autoencoder-based darknet traffic identification method (AE-DTI). First, AE-DTI maps the feature values to pixels of a two-dimensional grayscale image after deduplication and denoising of the darknet traffic dataset. Then, AE-DTI designs a new feature selection algorithm (AE-FS) to downscale the grayscale graph, and AE-FS trains a feature scoring network, which globally scores all the features based on the reconstruction error to select the features with scores greater than or equal to a set threshold value. Finally, AE-DTI uses a one-dimensional convolutional neural network with a dropout layer to identify darknet traffic on the basis of alleviating overfitting. Experimental results on the ISCXTor2016 dataset show that, compared with other dimensionality reduction methods (PCA, LLE, ISOMAP, and autoencoder), the classification model trained with the data obtained from AE-FS has a significant improvement in classification accuracy and classification efficiency. Moreover, AE-DTI also shows significant improvement in recognition accuracy compared with other models. Experimental results on the CSE-CIC-IDS2018 dataset and CIC-Darknet2020 dataset show that AE-DTI has strong generalization.

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.

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.