Reptile Search Algorithm （RSA） has strong global exploration ability， but its exploitation ability is relatively weak and it cannot converge well in the late stage of the iteration. To address the above issues， combined with the Teaching-Learning-Based Optimization （TLBO） algorithm， the Beetle Antennae Search （BAS） algorithm based on quadratic interpolation and the lens opposite-based learning strategy， Reptile Search Algorithm based on Multi-Hunting Coordination Strategy （MHCS-RSA） was proposed. In MHCS-RSA， the position update formula of the hunting cooperation in the encircling phase （global exploration） and hunting phase （local exploitation） of RSA was retained. And in the hunting coordination of the hunting phase， the learning phase of TLBO algorithm and the BAS based on quadratic interpolation were integrated to perform position update in order to improve the exploitation ability and convergence ability of the algorithm. In addition， the lens opposite-based learning strategy was introduced to enhance the algorithm ability of jumping out of the local optimum. Experimental results on CEC 2020 test functions show that MHCS-RSA has good optimization， convergence abilities and robustness. By solving the tension/compression spring design problem and the speed reducer design problem， the validity of MHCS-RSA is further verified in solving practical problems.

In response to several issues related to the interference of multiple aspect words in the syntactic dependency tree， redundant information caused by invalid words and punctuation marks， as well as weak correlations between aspect words and corresponding sentiment words， an aspect-level sentiment analysis model combining Strong Association Dependencies and Concise Syntax （SADCS） was proposed. Firstly， a sentiment Part-Of-Speech （POS） list was constructed to enhance the association between aspect words and corresponding sentiments. Then， a joint list incorporating POS list and dependency relationships was constructed to eliminate redundant information of invalid words and punctuation marks in the optimized dependency tree. Next， optimized dependency tree was combined with a Graph ATtention network （GAT） to model and extract contextual features. Finally， contextual feature information and the feature information of dependency relationship types were learned and fused to enhance the feature representation， enabling the classifier to efficiently predict the sentiment polarity of each aspect word. The proposed model was experimentally analyzed on four public datasets. Compared with the DMF-GAT-BERT （Dynamic Multichannel Fusion mechanism based on the GAT and BERT （Bidirectional Encoder Representations from Transformers）） model， the accuracy of the proposed model increased by 1.48， 1.81， 0.09 and 0.44 percentage points， respectively. Experimental results demonstrate that the proposed model effectively enhances the association between aspect words and sentiment words， resulting in more accurate prediction of aspect word sentiment polarity.

Focused on the issue that the current Aspect-Based Sentiment Analysis （ABSA） models rely too much on the syntactic dependency tree with relatively sparse relationships to learn feature representations， which leads to the insufficient ability of the model to learn local information， an ABSA model fused with multi-window local information called MWGAT （combining Multi-Window local information and Graph ATtention network） was proposed. Firstly， the local contextual features were learned through the multi-window local feature learning mechanism， and the potential local information contained in the text was mined. Secondly， Graph ATtention network （GAT）， which can better understand the syntactic dependency tree， was used to learn the syntactic structure information represented by the syntactic dependency tree， and syntax-aware contextual features were generated. Finally， these two types of features representing different semantic information were fused to form the feature representation containing both the syntactic information of syntactic dependency tree and the local information， so that the sentiment polarities of aspect words were discriminated by the classifier efficiently. Three public datasets， Restaurant， Laptop， and Twitter were used for experiment. The results show that compared with the T-GCN （Type-aware Graph Convolutional Network） model combined with the syntactic dependency tree， the proposed model has the Macro-F1 score improved by 2.48%， 2.37% and 0.32% respectively. It can be seen that the proposed model can mine potential local information effectively and predict the sentiment polarities of aspect words more accurately.

Aspect-oriented Fine-grained Opinion Extraction （AFOE） extracts aspect terms and opinion terms from reviews in the form of opinion pairs or additionally extracts sentiment polarities of aspect terms on the basis of the above to form opinion triplets. Aiming at the problem of neglecting correlation between the opinion pairs and contexts， an aspect-oriented Adaptive Span Feature-Grid Tagging Scheme （ASF-GTS） model was proposed. Firstly， BERT （Bidirectional Encode Representation from Transformers） model was used to obtain the feature representation of the sentence. Then， the correlation between the opinion pair and local context was enhanced by the Adaptive Span Feature （ASF） method. Next， Opinion Pair Extraction （OPE） was transformed into a uniform grid tagging task by Grid Tagging Scheme （GTS）. Finally， the corresponding opinion pairs or opinion triplet were generated by the specific decoding strategy. Experiments were carried out on four AFOE benchmark datasets adaptive to the task of opinion tuple extraction. The results show that compared with GTS-BERT （Grid Tagging Scheme-BERT） model， the proposed model has the F1-score improved by 2.42% to 7.30% and 2.62% to 6.61% on opinion pair or opinion triplet tasks， respectively. The proposed model can effectively reserve the sentiment correlation between opinion pair and context， and extract opinion pairs and their sentiment polarities more accurately.