An Aspect-Level Sentiment Based On Two-Channel Information Fusion Classification Model

Aspect-level sentiment classification is a fine-grained task in sentiment analysis,and its goal is to identify the sentiment attitude corresponding to a certain aspect of a sentence.This kind of aspect-specific sentiment extraction can provide more accurate data to support individuals,enterprises,and related departments in making decisions,and therefore has great practical significance.Most of the current sentiment classification models are constructed by extracting only one type of feature of text information,ignoring the fact that the fusion of multiple features can also improve the model’s effectiveness.To solve the above problems,this paper proposes a GCNN model based on two-channel information fusion.The main idea of model construction is as follows:(1)In aspect-level sentiment analysis tasks,traditional sentiment classification models tend to treat words as a single embedding and ignore lexically and location information of words,and thus are not satisfactory in model prediction performance.In this paper,we use a BERT pre-training model to enhance the sentiment-semantic representation of traditional word embeddings,while incorporating lexical and location information to obtain the final word vector representation to alleviate the problem of insufficient word vector representation.(2)In this paper,we design a Mul CNN module based on multigranularity convolutional operations to extract semantic features of the text.Unlike the commonly used single convolutional kernel,the module can use three different granularity convolutional kernels to extract N-gram features of text sequences at the same time,and the feature matrices extracted by the different granularity convolutional kernels are not the same.(3)Although N-gram features can well help the model to judge the sentiment polarity of aspect words,the deep learning model with single features does not consider the importance of syntactic structure,and the Dep GCN module based on syntactic dependencies is designed in this paper.The module first learns the sentence dependency relationship to get the logical distance between words by the dependency resolution model,and then uses words and dependencies as the initial nodes and edges of the graph convolutional neural network,respectively,to get a vector representation containing rich syntactic features.(4)Feature information is the key to the model’s ability to accurately predict sentiment polarity.Traditional models often perform simple splicing operations on feature vectors to fuse features,but such operations do not further explore the correlation between information.In this paper,we take into account both syntactic feature relevance and semantic feature complementarity and build an information fusion layer for two-channel feature fusion based on the attention mechanism,which achieves the ability to enhance feature vector representation.The word embedding representation after BERT pre-training contains richer feature information,and these high-quality word vectors can learn both semantic and syntactic features of the sentences through the dualchannel architecture,and realize effective feature interaction in the feature fusion layer to give full play to the complementary advantages of semantic and syntactic features and enhance the robustness of the model.When the sentence meaning is adequately expressed,the GCNN model can rely on the Mul CNN module containing semantic information to achieve sentiment classification;when the syntactic relationship of the sentence is obvious,the GCNN model can rely on the Dep CNN module containing syntactic information to achieve sentiment classification.By comparing the GCNN model with the selected 12 representative models on three public datasets,it can be seen that the model proposed in this paper performs well in terms of prediction accuracy and F1 value,and a series of experiments are also set up in this paper to prove that the GCNN model proposed in this paper can effectively mine and fuse feature information,which confirms the reliability and validity of the GCNN model.