Dynamic multiobjective optimization problems (DMOPs) frequently arise in real-world applications, where environments evolve over time. A central challenge in solving DMOPs is the ability to accurately and efficiently track optimal solutions as the environment changes. Existing dynamic multiobjective evolutionary algorithms (DMOEAs) based on single-strategy mechanisms often perform well only under specific environmental conditions, while multistrategy cooperative frameworks tend to suffer from delayed decision-making in rapidly changing, highly uncertain scenarios To address these shortcomings, this article introduces a fuzzy inference-based adaptive multistrategy-DMOEAs (FIAMS-DMOEA). This approach is designed to enhance real-time responsiveness to sudden environmental changes and mitigate decision-making latency. In the proposed method, a randomly dynamic fuzzy system serves as the foundation for population-level adaptability, enabling the algorithm to cope with highly uncertain environmental dynamics. This is further augmented by a multistrategy adaptive response mechanism (MSAR) that improves overall adaptability. By leveraging fuzzy rules, the algorithm can perceive environmental uncertainty in real time and design differentiated migration strategies, thereby establishing a robust foundation for subsequent optimization. The adaptive response mechanism centers on a multistrategy weight update method based on individual dimensional effects, which evaluates individual contributions from a dimension-specific perspective. Extensive experimental results on a suite of 32 benchmark DMOPs demonstrate that FIAMS-DMOEA consistently outperforms five state-of-the-art DMOEAs in overall performance.

ABSTRACT The integration of syntactic and semantic features for aspect‐level sentiment analysis has emerged as a prominent research trend in recent years, typically achieved by combining syntactic dependency trees with graph convolutional networks. However, most existing methods can only extract a single class of syntactic and semantic features. This study identifies sentiment‐supporting words as a means to extract a richer set of syntactic and semantic features. Accordingly, this paper introduces a sentiment support word‐based dependency graph convolutional network (SSW‐RDGCN) model. This model generates matrices by leveraging the dependency relationships and distance relationships between the nodes of the syntactic dependency tree. These matrices are then processed through specific functions to create two new matrices, which, along with the initial features of the sentence, are input into a graph convolutional network. Subsequently, three types of syntactic and semantic features extracted from the sentiment support words are incorporated, and the aspect word features and sentiment support word features are augmented. Experiments conducted on three publicly available datasets demonstrate that the proposed model outperforms the baseline model.

Summary Using imbalanced data in classification affect the accuracy. If the classification is based on imbalanced data directly, the results will have large deviations. A common approach to dealing with imbalanced data is to re‐structure the raw dataset via undersampling method. The undersampling method usually uses random or clustering approaches to trimming the majority class in the dataset, since some data in the majority class makes not contribute to classification model. In this paper a revised undersampling approach is proposed. First, we perform space compression in the vertical direction of the separating hyperplane. Then, a weighted random sampling hybrid ensemble learning method is carried out to make the sampled objects spread more widely near the separating hyperplane. Experiments with 7 under‐sampling methods on 21 imbalanced datasets show that our method has achieved good results.

Aspect-based sentiment classification is currently an important research direction to identify the sentiment expressed by sentences in different aspects. The primary approach for performing aspect-level sentiment analysis involves extracting both grammatical and semantic information. However, analyzing the grammatical connection between aspect words and other words within a review sentence using morphological features like part of speech can be exceedingly complex. This paper proposes the concept of sentiment-supporting words, dividing sentences into aspectual words, sentiment-supporting words and non-sentiment-supporting words, which simplifies the core task of sentiment analysis. Three rules are designed for determining the “sentiment-support words” of the text in different aspects. Subsequently, the application of sentiment-support words in sentiment analysis models is given, and five classical sentiment analysis models are improved accordingly. According to the experimental outcomes on two publicly available datasets, the “sentiment-support words” and corresponding sentiment support rules proposed in this paper are capable of significantly enhancing aspect-based sentiment analysis.