Agricultural and Forest Entomology

Aspect-based sentiment analysis is a fine-grained sentiment analysis task that involves classifying the sentiment polarity of specific aspect terms in sentences. Existing semantic graph convolutional networks fail to accurately capture the relationship between aspect terms and opinion words, thus overlooking attention to aspect terms and leading to inaccurate classification results. To address this issue, this paper proposes Aspect-Level Sentiment Analysis with Semantic and Emotional Modeling (ALSEM). The model theoretically establishes a framework that systematically explains the interaction between semantic information and sentiment knowledge, thereby guiding the design and method selection for the model. By integrating self-attention and aspect-aware attention mechanisms, ALSEM constructs an attention score matrix for sentences and uses graph convolutional networks to extract semantic features based on this matrix. The model captures both aspect-related semantic associations and global semantic information, providing comprehensive support for sentiment classification. Additionally, it incorporates external sentiment knowledge to enhance the interaction between aspect terms and opinion words. We conduct experiments on three benchmark datasets to evaluate the performance of the proposed model. Experimental results demonstrate that on the Restaurant14, Laptop14, and Twitter datasets, our proposed model achieves accuracy rates of 85.26%, 80.32%, and 76.72%, respectively.

In the current era of big data, which constantly generates massive data, all walks of life are producing a large number of multivariate time series data sets. The ability to predict these multivariate time series data sets is essential for effective decision making and management in industry. Considering that the relationship between variables of multivariable time series on different time scales is not the same, this paper proposes a multi-time scale prediction hybrid network (MSACN-LSTM) for multi-time scale multivariable time series prediction. Firstly, in order to explore the relationship between variables at multiple scales, we construct MSACN module. Specifically, we introduce CBAM attention mechanism based on TCN structure and add residual connection to form 4 branches with different convolution kernel sizes, and use these 4 branches to mine the relationship between variables of the down-sampled multivariate sequences. Secondly, in order to mine the time dependence of elements within variables at the same scale, we construct a two-layer LSTM module to extract the time features within the data series. In order to verify the validity of the model, we conducted ablation experiments on several public data sets, and the results show that the use of MSACN and LSTM modules can improve the prediction accuracy, because they can mine the temporal and spatial characteristics of multivariate serial data at different scales. In order to verify the superiority of this model over several different deep learning models, we also conducted comparative experiments. The experimental results show that the new model has good prediction effect and high prediction accuracy in single and multi-step prediction of multivariable sequences.

Abstract Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, numerous new models have emerged in the field of time series forecasting in recent years. However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets. To address this gap, in this review, we comprehensively study the previous works and summarize the general paradigms of Deep Time Series Forecasting (DTSF) in terms of model architectures. Besides, we take an innovative approach by focusing on the composition of time series and systematically explain important feature extraction methods. Additionally, we provide an overall compilation of datasets from various domains in existing works. Finally, we systematically emphasize the significant challenges faced and future research directions in this field.