Detection of hazardous events based on social media and news
With the rapid development of the Internet, social media is becoming more and more dominating in people’s daily life. On social media, users report and share their real-time observations with the world. Therefore, it can be used as a good source of real-time or close-to-real-time information. This i...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/141257 |
| _version_ | 1811677910364323840 |
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| author | Wu, Chuqiao |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Wu, Chuqiao |
| author_sort | Wu, Chuqiao |
| collection | NTU |
| description | With the rapid development of the Internet, social media is becoming more and more dominating in people’s daily life. On social media, users report and share their real-time observations with the world. Therefore, it can be used as a good source of real-time or close-to-real-time information. This information has great value and can be used in various ways. In this project, by using different artificial intelligence techniques, Twitter posts will be used to detect hazardous event. The whole project is divided into four parts. The first part is to extract data from twitter using twitter API for further use. The second part is classification. Using Long Short-Term Memory (LSTM) Model, posts are classified into different classes, such as earthquake, typhoon and shooting. Thirdly, after classification, unsupervised learning is performed to cluster posts related to a same event together. The posts in each class are embedded into Term Frequency–Inverse Document Frequency (TF- IDF) vectors and then clustered using K-Means Method. At last, for each cluster, we will use Spacy Named Entity Recognition method to extract useful information such as date, time and location to represent the hazardous event. |
| first_indexed | 2024-10-01T02:44:52Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/141257 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:44:52Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1412572023-07-07T18:02:44Z Detection of hazardous events based on social media and news Wu, Chuqiao Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Engineering::Computer science and engineering::Data Engineering::Electrical and electronic engineering With the rapid development of the Internet, social media is becoming more and more dominating in people’s daily life. On social media, users report and share their real-time observations with the world. Therefore, it can be used as a good source of real-time or close-to-real-time information. This information has great value and can be used in various ways. In this project, by using different artificial intelligence techniques, Twitter posts will be used to detect hazardous event. The whole project is divided into four parts. The first part is to extract data from twitter using twitter API for further use. The second part is classification. Using Long Short-Term Memory (LSTM) Model, posts are classified into different classes, such as earthquake, typhoon and shooting. Thirdly, after classification, unsupervised learning is performed to cluster posts related to a same event together. The posts in each class are embedded into Term Frequency–Inverse Document Frequency (TF- IDF) vectors and then clustered using K-Means Method. At last, for each cluster, we will use Spacy Named Entity Recognition method to extract useful information such as date, time and location to represent the hazardous event. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-05T05:40:11Z 2020-06-05T05:40:11Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141257 en A1120-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Data Engineering::Electrical and electronic engineering Wu, Chuqiao Detection of hazardous events based on social media and news |
| title | Detection of hazardous events based on social media and news |
| title_full | Detection of hazardous events based on social media and news |
| title_fullStr | Detection of hazardous events based on social media and news |
| title_full_unstemmed | Detection of hazardous events based on social media and news |
| title_short | Detection of hazardous events based on social media and news |
| title_sort | detection of hazardous events based on social media and news |
| topic | Engineering::Computer science and engineering::Data Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/141257 |
| work_keys_str_mv | AT wuchuqiao detectionofhazardouseventsbasedonsocialmediaandnews |