Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method
Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we p...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/6/935 |
| _version_ | 1797594927068086272 |
|---|---|
| author | Changlu Zhang Haojie Fan Jian Zhang Qiong Yang Liqian Tang |
| author_facet | Changlu Zhang Haojie Fan Jian Zhang Qiong Yang Liqian Tang |
| author_sort | Changlu Zhang |
| collection | DOAJ |
| description | Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted. |
| first_indexed | 2024-03-11T02:29:38Z |
| format | Article |
| id | doaj.art-4de308f09dd44af8a0c60da83279edb5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T02:29:38Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4de308f09dd44af8a0c60da83279edb52023-11-18T10:18:29ZengMDPI AGEntropy1099-43002023-06-0125693510.3390/e25060935Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic MethodChanglu Zhang0Haojie Fan1Jian Zhang2Qiong Yang3Liqian Tang4School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, ChinaBeijing Key Lab of Green Development Decision Based on Big Data, Beijing 100192, ChinaSchool of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, ChinaSchool of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, ChinaSchool of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, ChinaCurrently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.https://www.mdpi.com/1099-4300/25/6/935sentiment analysistopic discoveryFastTextinformation gainhierarchical clustering |
| spellingShingle | Changlu Zhang Haojie Fan Jian Zhang Qiong Yang Liqian Tang Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method Entropy sentiment analysis topic discovery FastText information gain hierarchical clustering |
| title | Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method |
| title_full | Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method |
| title_fullStr | Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method |
| title_full_unstemmed | Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method |
| title_short | Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method |
| title_sort | topic discovery and hotspot analysis of sentiment analysis of chinese text using information theoretic method |
| topic | sentiment analysis topic discovery FastText information gain hierarchical clustering |
| url | https://www.mdpi.com/1099-4300/25/6/935 |
| work_keys_str_mv | AT changluzhang topicdiscoveryandhotspotanalysisofsentimentanalysisofchinesetextusinginformationtheoreticmethod AT haojiefan topicdiscoveryandhotspotanalysisofsentimentanalysisofchinesetextusinginformationtheoreticmethod AT jianzhang topicdiscoveryandhotspotanalysisofsentimentanalysisofchinesetextusinginformationtheoreticmethod AT qiongyang topicdiscoveryandhotspotanalysisofsentimentanalysisofchinesetextusinginformationtheoreticmethod AT liqiantang topicdiscoveryandhotspotanalysisofsentimentanalysisofchinesetextusinginformationtheoreticmethod |