Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding
With the massive spike in the use of Online Social Network Sites (OSNSs) platforms such as Web 2.0, microblogs services and online blogs, etc., valuable information in the form of sentiment, thoughts, opinions, as well as epidemic outbreaks, etc. are transferred. With the OSNSs being widely accessib...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9223762/ |
| _version_ | 1818349865115058176 |
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| author | Samina Amin M. Irfan Uddin M. Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H. Alkinani |
| author_facet | Samina Amin M. Irfan Uddin M. Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H. Alkinani |
| author_sort | Samina Amin |
| collection | DOAJ |
| description | With the massive spike in the use of Online Social Network Sites (OSNSs) platforms such as Web 2.0, microblogs services and online blogs, etc., valuable information in the form of sentiment, thoughts, opinions, as well as epidemic outbreaks, etc. are transferred. With the OSNSs being widely accessible, this work aims at proposing a novel approach for disease (dengue or flu) detection based on social media posts. For this purpose, an automated approach is designed with the help of LSTM (Long Short Term Memory) and word embedding techniques. Then the performance of the proposed approach is validated using a set of standard evaluation matrices. In addition, the effectiveness of the selected models is evaluated with performance measurement techniques. The accuracy of the proposed research approach is evaluated using two word embedding techniques; Word2Vec with Skip-gram (SG) and Word2Vec with Continuous-bag-of-words (CBOW). Based on the results conducted in this paper the LSTM Word2Vec with CBOW achieved better results compared to LSTM with Word2Vec SG features embedding technique. Our findings prove that the proposed method yields 94% accuracy compared to state-of-the-art approaches. Consequently, LSTM performed better than other leading methods in the detection of disease-infected people in tweets. In the end, spatial analysis is performed to identify the disease infected region. |
| first_indexed | 2024-12-13T18:12:44Z |
| format | Article |
| id | doaj.art-45f4366f53d64840b10a5bab91284c0a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:12:44Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-45f4366f53d64840b10a5bab91284c0a2022-12-21T23:35:55ZengIEEEIEEE Access2169-35362020-01-01818905418906810.1109/ACCESS.2020.30311749223762Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word EmbeddingSamina Amin0M. Irfan Uddin1https://orcid.org/0000-0002-1355-3881M. Ali Zeb2Ala Abdulsalam Alarood3Marwan Mahmoud4https://orcid.org/0000-0002-0787-8225Monagi H. Alkinani5https://orcid.org/0000-0002-7658-7085Institute of Computing, Kohat University of Science and Technology, Kohat, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanCollege of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaFaculty of Applied Studies, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer science and Artificial Intelligence, College of Computer Sciences and Engineering, University of Jeddah, Jeddah, Saudi ArabiaWith the massive spike in the use of Online Social Network Sites (OSNSs) platforms such as Web 2.0, microblogs services and online blogs, etc., valuable information in the form of sentiment, thoughts, opinions, as well as epidemic outbreaks, etc. are transferred. With the OSNSs being widely accessible, this work aims at proposing a novel approach for disease (dengue or flu) detection based on social media posts. For this purpose, an automated approach is designed with the help of LSTM (Long Short Term Memory) and word embedding techniques. Then the performance of the proposed approach is validated using a set of standard evaluation matrices. In addition, the effectiveness of the selected models is evaluated with performance measurement techniques. The accuracy of the proposed research approach is evaluated using two word embedding techniques; Word2Vec with Skip-gram (SG) and Word2Vec with Continuous-bag-of-words (CBOW). Based on the results conducted in this paper the LSTM Word2Vec with CBOW achieved better results compared to LSTM with Word2Vec SG features embedding technique. Our findings prove that the proposed method yields 94% accuracy compared to state-of-the-art approaches. Consequently, LSTM performed better than other leading methods in the detection of disease-infected people in tweets. In the end, spatial analysis is performed to identify the disease infected region.https://ieeexplore.ieee.org/document/9223762/Social mediadisease detectiondeep learningWord2VecLSTM |
| spellingShingle | Samina Amin M. Irfan Uddin M. Ali Zeb Ala Abdulsalam Alarood Marwan Mahmoud Monagi H. Alkinani Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding IEEE Access Social media disease detection deep learning Word2Vec LSTM |
| title | Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding |
| title_full | Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding |
| title_fullStr | Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding |
| title_full_unstemmed | Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding |
| title_short | Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding |
| title_sort | detecting dengue flu infections based on tweets using lstm and word embedding |
| topic | Social media disease detection deep learning Word2Vec LSTM |
| url | https://ieeexplore.ieee.org/document/9223762/ |
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