Text Sentiment Analysis Based on Fusion of Attention Mechanism and BiGRU

Abstract

Aiming at the lack of the ability of simple neural networks to capture the contextual semantics of texts and extract important information in texts,a sentiment analysis model FFA-BiAGRU is proposed,which integrates attention mechanism and GRU.First,we pre-process the text and vectorize the words through GloVe to reduce the vector space dimension.Then,through a hybrid model that fuses the attention mechanism with the update gate of the gating unit,it can extract important information in the text features.Finally,the text features are further extracted through the forced forward attention mechanism,and then classified by the softmax classifier.Experiments on public data sets show that the algorithm can effectively improve the sentiment ana-lysis performance.

Keywords

• attention mechanism|gru|glove word vector|emotion analysis