Similar Word Replacement Method for Improving News Commenter Analysis
In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 milli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6803 |
| _version_ | 1797480743270612992 |
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| author | Deun Lee Sunoh Choi |
| author_facet | Deun Lee Sunoh Choi |
| author_sort | Deun Lee |
| collection | DOAJ |
| description | In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%. |
| first_indexed | 2024-03-09T22:04:29Z |
| format | Article |
| id | doaj.art-600e9c3e7246458bb1aa48b33dbc7e19 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:04:29Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-600e9c3e7246458bb1aa48b33dbc7e192023-11-23T19:42:56ZengMDPI AGApplied Sciences2076-34172022-07-011213680310.3390/app12136803Similar Word Replacement Method for Improving News Commenter AnalysisDeun Lee0Sunoh Choi1Department of Software Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Software Engineering, Jeonbuk National University, Jeonju 54896, KoreaIn Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%.https://www.mdpi.com/2076-3417/12/13/6803internet newsuser analysisword2vec |
| spellingShingle | Deun Lee Sunoh Choi Similar Word Replacement Method for Improving News Commenter Analysis Applied Sciences internet news user analysis word2vec |
| title | Similar Word Replacement Method for Improving News Commenter Analysis |
| title_full | Similar Word Replacement Method for Improving News Commenter Analysis |
| title_fullStr | Similar Word Replacement Method for Improving News Commenter Analysis |
| title_full_unstemmed | Similar Word Replacement Method for Improving News Commenter Analysis |
| title_short | Similar Word Replacement Method for Improving News Commenter Analysis |
| title_sort | similar word replacement method for improving news commenter analysis |
| topic | internet news user analysis word2vec |
| url | https://www.mdpi.com/2076-3417/12/13/6803 |
| work_keys_str_mv | AT deunlee similarwordreplacementmethodforimprovingnewscommenteranalysis AT sunohchoi similarwordreplacementmethodforimprovingnewscommenteranalysis |