Aspect-Based Sentiment Analysis （ABSA） is a fine-grained sentiment analysis task aiming to analyze sentiment polarity of specific aspect words in a given text. Existing ABSA methods use Graph Convolutional Network （GCN） to process syntactic and semantic information， but they treat all syntactic dependencies of aspect words equally， ignoring the impact of distant unrelated words on target aspect words， resulting in inappropriate weight allocation of target aspect words and viewpoint words， and insufficient extraction of semantic information. Aiming at these issues， an ABSA model integrating syntax and sentiment knowledge was proposed. Firstly， a reachability matrix was constructed according to syntactic information. Based on this， a syntactic enhancement graph was constructed by weighting the central position through the aspect words. Secondly， a semantic enhancement graph was constructed by external emotional knowledge and aspect enhancement， and graph convolution was used to fully model the syntactic enhancement graph and semantic enhancement graph， respectively， so as to form different feature channels. Thirdly， biaffine attention was used to integrate syntactic and semantic information effectively. Finally， average-pooling and concatenation operations were used to obtain the final feature vectors corresponding to the aspect words. Experimental results indicate that compared with the deep dependency aware graph convolutional network model — DA-GCN-BERT （deep Dependency Aware GCN+BERT（Bidirectional Encoder Representations from Transformers））， the proposed model achieves the accuracy improvements of 1.71， 1.41， 1.27， 0.17， and 0.43 percentage points on five publicly available datasets， respectively. It can be seen that the proposed model has strong applicability in the ABSA field.

Mining users’ multi-dimensional opinions on products from the complaint texts of new energy vehicles can provide support for product design decisions. Because the complaint text has the characteristics of high entity density and lengthy sentence structure， the existing methods for Aspect-Opinion Pair Extraction （AOPE） suffer from weak correlations between aspect terms and opinion terms. To address this problem， an Aspect-Opinion pair Extraction model based on Context Enhancement （AOE-CE） was proposed， fusing topic features and text features as contextual representation to enhance the correlations between entities. This model was consisted of an entity recognition module and a relation detection module. Firstly， in the entity recognition module， the text was encoded by using a pre-trained model and a part-of-speech tagging tool. Secondly， Bi-directional Long Short-Term Memory （Bi-LSTM） network combined with multi-head attention was employed to capture contextual information and then derive text features. Subsequently， these text features were input into a Conditional Random Field （CRF） model to obtain the entity set. In the relation detection module， the topic features were obtained through BERT （Bidirectional Encoder Representations from Transformers） and fused with the text features to obtain the enhanced contextual representation. Then the tri-affine mechanism was used to enhance the correlations between entities with the help of contextual representation. Finally， the extraction result was obtained by Sigmoid. The experimental results show that the precision， recall， and F1 value of AOE-CE are 2.19， 1.08， and 1.60 percentage points higher than those of SDRN （Synchronous Double-channel Recurrent Network） model respectively， indicating that AOE-CE has better AOPE effect.

A complex causal relationship extraction model based on prompt enhancement and Bi-Graph ATtention network （BiGAT） — PE-BiGAT （Prompt Enhancement and Bi-Graph Attention Network） was proposed to address the issues of insufficient external information and information transmission forgetting caused by the high density and long sentence patterns of complex causal sentences. Firstly， the result entities from the sentence were extracted and combined with the prompt learning template to form the prompt information， and the prompt information was enhanced through an external knowledge base. Then， the prompt information was input into the BiGAT， the attention layer was combined with syntax and semantic dependency graphs， and the biaffine attention mechanism was used to alleviate feature overlapping and enhance the model’s perception of relational features. Finally， all causal entities in the sentence were predicted iteratively by the classifier， and all causal pairs in the sentence were analyzed through a scoring function. Experimental results on SemEval-2010 task 8 and AltLex datasets show that compared with RPA-GCN （Relationship Position and Attention?Graph Convolutional Network）， the proposed model improves the F1 score by 1.65 percentage points， with 2.16 and 4.77 percentage points improvements in chain causal and multi-causal sentences， which confirming that the proposed model has an advantage in dealing with complex causal sentences.

As one of the basic tasks of natural language processing， new word identification provides theoretical support for the establishment of Chinese dictionary and analysis of word sentiment tendency. However， the current new word identification methods do not consider the homophonic neologism identification， resulting in low precision of homophonic neologism identification. To solve this problem， a Chinese homophonic neologism discovery method based on Pinyin similarity was proposed， and the precision of homophonic neologism identification was improved by introducing the phonetic comparison of new and old words in this method. Firstly， the text was preprocessed， the Average Mutual Information （AMI） was calculated to determine the degree of internal cohesion of candidate words， and the improved branch entropy was used to determine the boundaries of candidate new words. Then， the retained words were transformed into Chinese Pinyin with similar pronunciations and compared to the Chinese Pinyin of the old words in the Chinese dictionary， and the most similar results of comparisons would be retained. Finally， if a comparison result exceeded the threshold， the new word in the result was taken as the homophonic neologism， and its corresponding word was taken as the original word. Experimental results on self built Weibo datasets show that compared with BNshCNs （Blended Numeric and symbolic homophony Chinese Neologisms） and DSSCNN （similarity computing model based on Dependency Syntax and Semantics）， the proposed method has the precision， recall and F1 score improved by 0.51 and 5.27 percentage points， 2.91 and 6.31 percentage points， 1.75 and 5.81 percentage points respectively， indicating that the proposed method has better Chinese homophonic neologism identification effect.