AUTHOR=Zhang Baohua , Zhang Huaping , Shang Jianyun , Cai Jiahao TITLE=An Augmented Neural Network for Sentiment Analysis Using Grammar JOURNAL=Frontiers in Neurorobotics VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.897402 DOI=10.3389/fnbot.2022.897402 ISSN=1662-5218 ABSTRACT=

Understanding human sentiment from their expressions is very important in human-robot interaction. But deep learning models are hard to represent grammatical changes for natural language processing (NLP), especially for sentimental analysis, which influence the robot's judgment of sentiment. This paper proposed a novel sentimental analysis model named MoLeSy, which is an augmentation of neural networks incorporating morphological, lexical, and syntactic knowledge. This model is constructed from three concurrently processed classical neural networks, in which output vectors are concatenated and reduced with a single dense neural network layer. The models used in the three grammatical channels are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected dense neural networks. The corresponding output in the three channels is morphological, lexical, and syntactic results, respectively. Experiments are conducted on four different sentimental analysis corpuses, namely, hotel, NLPCC2014, Douban movie reviews dataset, and Weibo. MoLeSy can achieve the best performance over previous state-of-art models. It indicated that morphological, lexical, and syntactic grammar can augment the neural networks for sentimental analysis.