GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

Abstract

Currently, attention mechanisms are widely used in aspect-level sentiment analysis tasks. Previous studies have only used attention mechanisms combined with neural networks for aspect-level sentiment classification, and the feature extraction of the model is insufficient. When the same aspect and sentiment polarity appear in multiple sentences, the semantic information sharing of the same domain is also ignored, resulting in low model performance. To address these problems, the paper proposes an aspect-level sentiment analysis model, GCAT-GTCU, which combines a Graph-connected Attention Network containing symmetry with Gate Than Change Unit. Three nodes of words, sentences, and aspects are constructed, and local and deep-level features of sentences are extracted using CNN splicing BiGRU; node connection information is added to GAT to form a GCAT containing symmetry to realize the information interaction of three nodes, pay attention to the contextual information, and update the shared information of three nodes at any time; a new gating mechanism GTCU is constructed to filter noisy information and control the flow of sentiment information; finally, the three nodes are extracted information to predict the final sentiment polarity. The experimental results on four publicly available datasets show that the model outperforms the baseline model against which it is compared in some very controlled situations.