Research And Application Of Text Sentiment Analysis Based On Dynamic Link Network

Recently,ChatGPT has become very popular,which has sparked heated discussions on AI applications.Among them,companion robots are a hot topic of discussion,and sentiment analysis technology is one of the cores of this application.Sentiment analysis technology based on human linguistics can optimize the human-computer interaction experience,and currently has great potential for development in the fields of education and elderly care.The aim of this article is to explore the research background,significance,and technological development of the task of text sentiment analysis.The article starts by introducing the definition and research significance of sentiment analysis tasks,as well as the construction methods and usage of sentiment lexicons,text feature representation methods,and commonly used deep learning models.In order to reflect the semantic differences of different morphemes,this paper proposes a sentiment analysis model using a dynamic attention mechanism.This mechanism calculates the similarity between context words and different morphemes in the aspect term,and generates a weight matrix.In order to distinguish the influence of different morphemes in the aspect aspect on discriminating the emotional tendency of the text.In addition,this paper introduces feature gating unit and position encoding mechanism to enhance feature representation and further improve the accuracy of sentiment analysis.Moreover,we proposed a multimodal-based dual-map sentiment analysis model to break the limitation of single-modal data in emotion expression.The model learns the emotional and semantic information of multimodal,and generates a joint vector through the fusion mechanism,which is used to enhance text feature information.At the same time,by introducing a domain-specific knowledge graph,this paper can provide more domain-related knowledge for the sentiment analysis model,thereby supporting the model to perform domain adaptation and improving the model’s sentiment analysis capability in this field.This paper uses the kingdom algorithm in the graph knowledge mining technology to extract a cross-domain knowledge fusion graph from the ConceptNet graph.Next,graph representations are generated using relational graph convolutional networks and used for downstream tasks.This further enhances the sentiment analysis capabilities of the model.Finally,the two proposed methods are applied to a public opinion analysis system,and the various functions of the system are briefly introduced,along with a demonstration of its relevant interfaces to display the completeness of the system.Through the exposition in this article,readers can understand the theoretical basis,relevant technical models,and their applications for sentiment analysis,providing important references for sentiment analysis research and practice.