ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis.

ABSTRACT: Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP), especially in sentiment analysis. NLP can execute numerous functions to achieve significant results through RNN and CNN. Likewise, previous research shows that RNN achieved meaningful results than CNN due to extracting long-term dependencies. Meanwhile, CNN has its advantage; it can extract high-level features using its local fixed-size context at the input level. However, integrating these advantages into one network is challenging because of overfitting in training. Another problem with such models is the consideration of all the features equally. To this end, we propose an attention-based sentiment analysis using CNN and two independent bidirection

SUBMITTER: Kamyab M 

PROVIDER: S-EPMC9044316 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature