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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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 |
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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 |
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