Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF

Abstract

Aiming at the problem that the Aspect-based sentiment analysis in Chinese has low recognition rate due to many steps, this paper proposes an improved BiLSTM-CRF model based on combine the Chinese character vector and Chinese words position feature, which can extract attribute words and sentiment words jointly simultaneously, while extracting Polarity judges of sentiment words. Experiments show that the improved model improves the precision rate by 9.2% 13.32%, recall rate improves 0.48% 21.29%, F-measure improves 7.33% 15.74% compared with Conditional Random Fields (CRF) model and Long Short Term Memory (LSTM) model on the self-built 6357 mobile reviews dataset. The experimental results show that the model improves the accuracy of Aspect-based sentiment analysis and can effectively obtain the information required by users need in evaluation texts.