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CREATION
Title : Traffic Event Detection from Twitter Using a Combination of CNN and BERT
Author :

GREGORIUS ARIA NERUDA (1) Drs. Edi Winarko, M.Sc.,Ph.D. (2)
Date : 0 2021
Keyword : BERT, CNN, text classification, multi-label text classification, traffic event detection, tweet classification BERT, CNN, text classification, multi-label text classification, traffic event detection, tweet classification
Abstract : Knowing traffic situations in a real-time manner is essential in modern society. There are several challenges to using conventional physical sensors. The rise of social media can be an alternative solution to this problem, as it can be a low-cost but still reliable source of information, one of which is Twitter. This paper proposes a combination of CNN (classifier) and BERT (feature extraction) to detect traffic events using social media data from Twitter. Our experimental results show that using the contextual word embedding BERT helps understand the context in tweets and gives better results than non-contextualized word embedding.
Group of Knowledge :
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 6-6-Traffic Event Detection from Twitter Using a Combination of CNN and BERT.pdf
Document Type : Cek Similarity
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2 front matter.pdf
Document Type : Seminar Sampul Prosiding
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3 Table_of_Content.pdf
Document Type : Daftar Isi
Daftar Isi View
4 2021-ICACSIS-traffict event detection from twitter using CNN and BERT-NW.pdf
Document Type : Artikel dan Sertifikat/Bukti Kehadiran/Pasport (jika tidak ada sertifikat)
Artikel dan Sertifikat/Bukti Kehadiran/Pasport (jika tidak ada sertifikat) View