Cargando…

An Augmented Neural Network for Sentiment Analysis Using Grammar

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. Th...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Baohua, Zhang, Huaping, Shang, Jianyun, Cai, Jiahao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284271/
https://www.ncbi.nlm.nih.gov/pubmed/35845762
http://dx.doi.org/10.3389/fnbot.2022.897402
_version_ 1784747526740508672
author Zhang, Baohua
Zhang, Huaping
Shang, Jianyun
Cai, Jiahao
author_facet Zhang, Baohua
Zhang, Huaping
Shang, Jianyun
Cai, Jiahao
author_sort Zhang, Baohua
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9284271
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92842712022-07-16 An Augmented Neural Network for Sentiment Analysis Using Grammar Zhang, Baohua Zhang, Huaping Shang, Jianyun Cai, Jiahao Front Neurorobot Neuroscience 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. Frontiers Media S.A. 2022-07-01 /pmc/articles/PMC9284271/ /pubmed/35845762 http://dx.doi.org/10.3389/fnbot.2022.897402 Text en Copyright © 2022 Zhang, Zhang, Shang and Cai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhang, Baohua
Zhang, Huaping
Shang, Jianyun
Cai, Jiahao
An Augmented Neural Network for Sentiment Analysis Using Grammar
title An Augmented Neural Network for Sentiment Analysis Using Grammar
title_full An Augmented Neural Network for Sentiment Analysis Using Grammar
title_fullStr An Augmented Neural Network for Sentiment Analysis Using Grammar
title_full_unstemmed An Augmented Neural Network for Sentiment Analysis Using Grammar
title_short An Augmented Neural Network for Sentiment Analysis Using Grammar
title_sort augmented neural network for sentiment analysis using grammar
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284271/
https://www.ncbi.nlm.nih.gov/pubmed/35845762
http://dx.doi.org/10.3389/fnbot.2022.897402
work_keys_str_mv AT zhangbaohua anaugmentedneuralnetworkforsentimentanalysisusinggrammar
AT zhanghuaping anaugmentedneuralnetworkforsentimentanalysisusinggrammar
AT shangjianyun anaugmentedneuralnetworkforsentimentanalysisusinggrammar
AT caijiahao anaugmentedneuralnetworkforsentimentanalysisusinggrammar
AT zhangbaohua augmentedneuralnetworkforsentimentanalysisusinggrammar
AT zhanghuaping augmentedneuralnetworkforsentimentanalysisusinggrammar
AT shangjianyun augmentedneuralnetworkforsentimentanalysisusinggrammar
AT caijiahao augmentedneuralnetworkforsentimentanalysisusinggrammar