Traffic Event Detection from Twitter Using a Combination of CNN and BERT

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 whic...

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Bibliographic Details
Published in2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS) pp. 1 - 7
Main Authors Neruda, Gregorius Aria, Winarko, Edi
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.10.2021
Subjects
BERT
Bit error rate
Blogs
CNN
multi-label text classification
Real-time systems
Reliability
Sensors
Social networking (online)
Text categorization
text classification
traffic event detection
tweet classification
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Summary: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.
DOI:10.1109/ICACSIS53237.2021.9631334