With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

Research on uni-modal sentiment analysis has achieved great success, but emotions in real life are mostly multi-modal; there are not only texts but also images, audio, video, and other forms. The various modes play a role in mutual promotion. If the connection between various modalities can be mined, the accuracy of sentiment analysis will be further improved. To this end, this paper introduces a cross-attention-based multi-modal fusion model for images and text, namely, MCAM. First, we use the ALBert pre-training model to extract text features for text; then, we use BiLSTM to extract text context features; then, we use DenseNet121 to extract image features for images; and then, we use CBAM to extract specific areas related to emotion in images. Finally, we utilize multi-modal cross-attention to fuse the extracted features from the text and image, and we classify the output to determine the emotional polarity. In the experimental comparative analysis of MVSA and TumEmo public datasets, the model in this article is better than the baseline model, with accuracy and F1 scores reaching 86.5% and 75.3% and 85.5% and 76.7%, respectively. In addition, we also conducted ablation experiments, which confirmed that sentiment analysis with multi-modal fusion is better than single-modal sentiment analysis.

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.

Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of Chinese text presents a formidable obstacle to overcome. To address prior inadequacies, the three-classification method is employed and a novel AB-CNN model is proposed, incorporating an attention mechanism, BiLSTM, and a CNN. The proposed model was tested on a public e-commerce dataset and demonstrated a superior performance compared to existing classifiers. It utilizes a word vector model to extract features from sentences and vectorize them. The attention layer is used to calculate the weighted average attention of each text, and the relevant representation is obtained. BiLSTM is then employed to read the text information from both directions, further enhancing the emotional level. Finally, softmax is used to classify the emotional polarity.

Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA–disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional experimental methods for exploring CDAs are time-consuming and resource-intensive, while existing computational models often struggle with the sparsity of CDA data and fail to uncover potential associations effectively. To address these challenges, we propose a novel CDA prediction method named the Graph Isomorphism Transformer with Dual-Stream Neural Predictor (GIT-DSP), which leverages knowledge graph technology to address data sparsity and predict CDAs more effectively. Specifically, the model incorporates multiple associations between circRNAs, diseases, and other non-coding RNAs (e.g., lncRNAs, and miRNAs) to construct a multi-source heterogeneous knowledge graph, thereby expanding the scope of CDA exploration. Subsequently, a Graph Isomorphism Transformer model is proposed to fully exploit both local and global association information within the knowledge graph, enabling deeper insights into potential CDAs. Furthermore, a Dual-Stream Neural Predictor is introduced to accurately predict complex circRNA–disease associations in the knowledge graph by integrating dual-stream predictive features. Experimental results demonstrate that GIT-DSP outperforms existing state-of-the-art models, offering valuable insights for precision medicine and disease-related research.

With the continuous rise of information technology and social networks, and the explosive growth of network text information, text sentiment analysis technology now plays a vital role in public opinion monitoring and product development analysis on networks. Text data are high-dimensional and complex, and traditional binary classification can only classify sentiment from positive or negative aspects. This does not fully cover the various emotions of users, and, therefore, natural language semantic sentiment analysis has limitations. To solve this deficiency, we propose a new model for analyzing text sentiment that combines deep learning and the bidirectional encoder representation from transformers (BERT) model. We first use an advanced BERT language model to convert the input text into dynamic word vectors; then, we adopt a convolutional neural network (CNN) to obtain the relatively significant partial emotional characteristics of the text. After extraction, we use the bidirectional recurrent neural network (BiGRU) to bidirectionally capture the contextual feature message of the text. Finally, with the MultiHeadAttention mechanism we obtain correlations among the data in different information spaces from different subspaces so that the key information related to emotion in the text can be selectively extracted. The final emotional feature representation obtained is classified using Softmax. Compared with other similar existing methods, our model in this research paper showed a good effect in comparative experiments on an e-commerce text dataset, and the accuracy and F1-score of the classification were significantly improved.