Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets
Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disrupti...
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| Format: | Article |
| Language: | English |
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MMU Press
2024-02-01
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| Series: | Journal of Informatics and Web Engineering |
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| Online Access: | https://journals.mmupress.com/index.php/jiwe/article/view/622 |
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| author | Noraysha Yusuf Maizatul Akmar Ismail Tasnim M.A. Zayet Kasturi Dewi Varathan Rafidah MD Noor |
| author_facet | Noraysha Yusuf Maizatul Akmar Ismail Tasnim M.A. Zayet Kasturi Dewi Varathan Rafidah MD Noor |
| author_sort | Noraysha Yusuf |
| collection | DOAJ |
| description | Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%. |
| format | Article |
| id | doaj-art-70e1683f0f9b4fc4adffb8625b06e829 |
| institution | Kabale University |
| issn | 2821-370X |
| language | English |
| publishDate | 2024-02-01 |
| publisher | MMU Press |
| record_format | Article |
| series | Journal of Informatics and Web Engineering |
| spelling | doaj-art-70e1683f0f9b4fc4adffb8625b06e8292024-12-08T04:12:25ZengMMU PressJournal of Informatics and Web Engineering2821-370X2024-02-013111410.33093/jiwe.2024.3.1.1.1621Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish TweetsNoraysha Yusuf0Maizatul Akmar Ismail1Tasnim M.A. Zayet2https://orcid.org/0000-0001-5755-5953Kasturi Dewi Varathan3Rafidah MD Noor4Universiti Malaya, MalaysiaUniversiti Malaya, MalaysiaUniversiti Malaya, MalaysiaUniversiti Malaya, MalaysiaUniversiti Malaya, MalaysiaRapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%.https://journals.mmupress.com/index.php/jiwe/article/view/622rapid transitldamanglishmultilingualtwitter |
| spellingShingle | Noraysha Yusuf Maizatul Akmar Ismail Tasnim M.A. Zayet Kasturi Dewi Varathan Rafidah MD Noor Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets Journal of Informatics and Web Engineering rapid transit lda manglish multilingual |
| title | Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets |
| title_full | Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets |
| title_fullStr | Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets |
| title_full_unstemmed | Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets |
| title_short | Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets |
| title_sort | term standardisation with lda model to detect service disruption events using english and manglish tweets |
| topic | rapid transit lda manglish multilingual |
| url | https://journals.mmupress.com/index.php/jiwe/article/view/622 |
| work_keys_str_mv | AT norayshayusuf termstandardisationwithldamodeltodetectservicedisruptioneventsusingenglishandmanglishtweets AT maizatulakmarismail termstandardisationwithldamodeltodetectservicedisruptioneventsusingenglishandmanglishtweets AT tasnimmazayet termstandardisationwithldamodeltodetectservicedisruptioneventsusingenglishandmanglishtweets AT kasturidewivarathan termstandardisationwithldamodeltodetectservicedisruptioneventsusingenglishandmanglishtweets AT rafidahmdnoor termstandardisationwithldamodeltodetectservicedisruptioneventsusingenglishandmanglishtweets |