Sentiment analysis method of comment text based on word vector with sentiment information

In order to solve the problem of low accuracy of sentiment classification caused by neglecting the sentiment information of words in distributed word representation method,an improved sentiment analysis method incorporating weighted word vectors of sentiment information was proposed.According to the...

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Autores principales:	Meiyuan LYU, Yongjian ZHANG, Yongqiang ZHANG, Shengjuan SUN
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	natural language processing; semantic rules; sentiment information; tf-idf; word2vec; weighted word vector; sentiment analysis Technology T
Acceso en línea:	https://doaj.org/article/95fdc59acfde481bb9803182e45f8ea8
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spelling	oai:doaj.org-article:95fdc59acfde481bb9803182e45f8ea82021-11-23T07:08:58ZSentiment analysis method of comment text based on word vector with sentiment information1008-154210.7535/hbkd.2021yx04008https://doaj.org/article/95fdc59acfde481bb9803182e45f8ea82021-08-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202104008&flag=1&journal_https://doaj.org/toc/1008-1542In order to solve the problem of low accuracy of sentiment classification caused by neglecting the sentiment information of words in distributed word representation method,an improved sentiment analysis method incorporating weighted word vectors of sentiment information was proposed.According to the exclusive domain sentiment dictionary,combined with the dictionary and semantic rules,the sentiment information is integrated into the TF-IDF algorithm,and the weighted word vector representation method is obtained by using word2vec model.The method is used to compare the collected comments of tourist attractions in Hebei Province with the control group.The results show that compared with the sentiment analysis method based on distributed word vector representation,the accuracy and recall rate of positive text are increased by 61% and 66%,and the F value reached 903%,the accuracy and recall rate of negative text are increased by 60% and 72%,and the F value reached 896% by using the improved method of sentiment analysis integrated with sentiment information weighted word vector.Therefore,the improved method of sentiment analysis integrated with sentiment information weighted word vector can effectively improve the accuracy of sentiment analysis of comment text,and provide valuable reference for users to obtain more accurate comments.Meiyuan LYUYongjian ZHANGYongqiang ZHANGShengjuan SUNHebei University of Science and Technologyarticlenatural language processing; semantic rules; sentiment information; tf-idf; word2vec; weighted word vector; sentiment analysisTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 4, Pp 380-388 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	natural language processing; semantic rules; sentiment information; tf-idf; word2vec; weighted word vector; sentiment analysis Technology T
spellingShingle	natural language processing; semantic rules; sentiment information; tf-idf; word2vec; weighted word vector; sentiment analysis Technology T Meiyuan LYU Yongjian ZHANG Yongqiang ZHANG Shengjuan SUN Sentiment analysis method of comment text based on word vector with sentiment information
description	In order to solve the problem of low accuracy of sentiment classification caused by neglecting the sentiment information of words in distributed word representation method,an improved sentiment analysis method incorporating weighted word vectors of sentiment information was proposed.According to the exclusive domain sentiment dictionary,combined with the dictionary and semantic rules,the sentiment information is integrated into the TF-IDF algorithm,and the weighted word vector representation method is obtained by using word2vec model.The method is used to compare the collected comments of tourist attractions in Hebei Province with the control group.The results show that compared with the sentiment analysis method based on distributed word vector representation,the accuracy and recall rate of positive text are increased by 61% and 66%,and the F value reached 903%,the accuracy and recall rate of negative text are increased by 60% and 72%,and the F value reached 896% by using the improved method of sentiment analysis integrated with sentiment information weighted word vector.Therefore,the improved method of sentiment analysis integrated with sentiment information weighted word vector can effectively improve the accuracy of sentiment analysis of comment text,and provide valuable reference for users to obtain more accurate comments.
format	article
author	Meiyuan LYU Yongjian ZHANG Yongqiang ZHANG Shengjuan SUN
author_facet	Meiyuan LYU Yongjian ZHANG Yongqiang ZHANG Shengjuan SUN
author_sort	Meiyuan LYU
title	Sentiment analysis method of comment text based on word vector with sentiment information
title_short	Sentiment analysis method of comment text based on word vector with sentiment information
title_full	Sentiment analysis method of comment text based on word vector with sentiment information
title_fullStr	Sentiment analysis method of comment text based on word vector with sentiment information
title_full_unstemmed	Sentiment analysis method of comment text based on word vector with sentiment information
title_sort	sentiment analysis method of comment text based on word vector with sentiment information
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/95fdc59acfde481bb9803182e45f8ea8
work_keys_str_mv	AT meiyuanlyu sentimentanalysismethodofcommenttextbasedonwordvectorwithsentimentinformation AT yongjianzhang sentimentanalysismethodofcommenttextbasedonwordvectorwithsentimentinformation AT yongqiangzhang sentimentanalysismethodofcommenttextbasedonwordvectorwithsentimentinformation AT shengjuansun sentimentanalysismethodofcommenttextbasedonwordvectorwithsentimentinformation
_version_	1718416841472212992

Sentiment analysis method of comment text based on word vector with sentiment information

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