Text Based Approach For Similar Traffic Incident Detection from Twitter
Microblog has been used as an information source to detect real-world event. Several related studies retrieved road traffic event based on textual content. Not only detect traffic incident, we found that it is necessary to recognize statuses with similar traffic incident content. Better representati...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Udayana University, Institute for Research and Community Services
2018-09-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/38749 |
| _version_ | 1798046017288929280 |
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| author | Myrna ermawati Joko Lianto Buliali |
| author_facet | Myrna ermawati Joko Lianto Buliali |
| author_sort | Myrna ermawati |
| collection | DOAJ |
| description | Microblog has been used as an information source to detect real-world event. Several related studies retrieved road traffic event based on textual content. Not only detect traffic incident, we found that it is necessary to recognize statuses with similar traffic incident content. Better representation of traffic information will help the handling of traffic incident by related parties. This study proposes text-based approach for identification of similar traffic incident from twitter posts. The proposed approach performs traffic incident information extraction and calculates information’s weight based on textual similarity upon traffic incident information gained. We evaluate the proposed method by using a traffic incident information retrieval system. We used Indonesian language corpus contains traffic incident tweets data. Best average f-measure 70% was achieved by retrieval system that tested using Jaccard coefficient. Therefore text matching such as Jaccard coefficient is more suitable to be implemented in very short text document such as extracted tweet document. The experiment result gives the conclusion that the proposed approach can be implemented for identification of similar traffic incident information from Twitter. |
| first_indexed | 2024-04-11T23:30:42Z |
| format | Article |
| id | doaj.art-f93bc0ddd7a04788b7d5596e22dccd5c |
| institution | Directory Open Access Journal |
| issn | 2088-1541 2541-5832 |
| language | English |
| last_indexed | 2024-04-11T23:30:42Z |
| publishDate | 2018-09-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj.art-f93bc0ddd7a04788b7d5596e22dccd5c2022-12-22T03:57:08ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322018-09-01637110.24843/LKJITI.2018.v09.i02.p0138749Text Based Approach For Similar Traffic Incident Detection from TwitterMyrna ermawati0Joko Lianto Buliali1authorDepartment of Informatics, Institut Teknologi Sepuluh Nopember (ITS)Microblog has been used as an information source to detect real-world event. Several related studies retrieved road traffic event based on textual content. Not only detect traffic incident, we found that it is necessary to recognize statuses with similar traffic incident content. Better representation of traffic information will help the handling of traffic incident by related parties. This study proposes text-based approach for identification of similar traffic incident from twitter posts. The proposed approach performs traffic incident information extraction and calculates information’s weight based on textual similarity upon traffic incident information gained. We evaluate the proposed method by using a traffic incident information retrieval system. We used Indonesian language corpus contains traffic incident tweets data. Best average f-measure 70% was achieved by retrieval system that tested using Jaccard coefficient. Therefore text matching such as Jaccard coefficient is more suitable to be implemented in very short text document such as extracted tweet document. The experiment result gives the conclusion that the proposed approach can be implemented for identification of similar traffic incident information from Twitter.https://ojs.unud.ac.id/index.php/lontar/article/view/38749 |
| spellingShingle | Myrna ermawati Joko Lianto Buliali Text Based Approach For Similar Traffic Incident Detection from Twitter Lontar Komputer |
| title | Text Based Approach For Similar Traffic Incident Detection from Twitter |
| title_full | Text Based Approach For Similar Traffic Incident Detection from Twitter |
| title_fullStr | Text Based Approach For Similar Traffic Incident Detection from Twitter |
| title_full_unstemmed | Text Based Approach For Similar Traffic Incident Detection from Twitter |
| title_short | Text Based Approach For Similar Traffic Incident Detection from Twitter |
| title_sort | text based approach for similar traffic incident detection from twitter |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/38749 |
| work_keys_str_mv | AT myrnaermawati textbasedapproachforsimilartrafficincidentdetectionfromtwitter AT jokoliantobuliali textbasedapproachforsimilartrafficincidentdetectionfromtwitter |