Triple-Channel Feature Mixed Sentiment Analysis Model Based on Attention Mechanism

Sentiment analysis is an important branch of the field of natural language processing, and opinion sentiment analysis is the current research hotspot. With the development of artificial intelligence, it is very challenging to effectively extract important emotional information from a large amount of text information and analyzes the emotional tendency of views in response to the increasing number of emotional opinions with rich connotations. Today’s methods mostly use shallow emotional factors, while ignoring deeper text semantic information, and cannot explore the semantic connection between words. To make up for this deficiency, this paper proposes a triple-channel feature-mixed sentiment analysis model Tri-BiGRU-Atten based on attention mechanism. This model combines different semantic feature mixed modeling to enable it to mine deeper emotional information in the context. Compared with the traditional attention mechanism, LSTM, Bi-LSTM, and other models, the emotion classification effect is more effective.