Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features

This paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentimen...

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Main Author: Minxia Zhu
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2022/9879986
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author Minxia Zhu
author_facet Minxia Zhu
author_sort Minxia Zhu
collection DOAJ
description This paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentiment value feature, expression feature, and improved semantic feature. A text sentiment classification model is formed by fusing multiple features. Compared with the traditional model and other single-feature models on the self-built dataset, the experimental results show that its sentiment classification ability has been effectively improved. The results show that the accuracy, recall, and F1 value of the fused multilevel feature MFCNN model are much higher than the accuracy, recall, and F1 value of other models. This also shows that the improved model of this method has a better effect of improving the accuracy.
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institution Kabale University
issn 1607-887X
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Discrete Dynamics in Nature and Society
spelling doaj-art-80efcb3a934043b7ab0e438a83d8c0be2025-02-03T01:07:37ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/9879986Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel FeaturesMinxia Zhu0School of LiteratureThis paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentiment value feature, expression feature, and improved semantic feature. A text sentiment classification model is formed by fusing multiple features. Compared with the traditional model and other single-feature models on the self-built dataset, the experimental results show that its sentiment classification ability has been effectively improved. The results show that the accuracy, recall, and F1 value of the fused multilevel feature MFCNN model are much higher than the accuracy, recall, and F1 value of other models. This also shows that the improved model of this method has a better effect of improving the accuracy.http://dx.doi.org/10.1155/2022/9879986
spellingShingle Minxia Zhu
Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
Discrete Dynamics in Nature and Society
title Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
title_full Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
title_fullStr Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
title_full_unstemmed Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
title_short Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features
title_sort sentiment analysis of international and foreign chinese language texts with multilevel features
url http://dx.doi.org/10.1155/2022/9879986
work_keys_str_mv AT minxiazhu sentimentanalysisofinternationalandforeignchineselanguagetextswithmultilevelfeatures