Improving a text classifier using text augmentation: road traffic content from Twitter

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dc.contributor.authorRaksachat, Thawatchai
dc.contributor.authorChawuthai, Rathachai
dc.date.accessioned2026-08-06T10:39:53Z
dc.date.available2026-08-06T10:39:53Z
dc.date.issued2023-01-01
dc.description.abstractThe purpose of this study is to develop a more effective method for categorizing Thai-language tweets related to traffic. The categorization consists of five categories. Previous studies have utilized CNN and BERT for classification, but have faced the challenge of needing balanced data for improved performance. To address this, we propose the use of BPEmb to augmentation the data and calculate cosine similarity. The subsequent step will be to create a balanced dataset to train a combination of CNN and bi-LSTM models for tweet classification. Our experiment demonstrates a significant improvement in tweet classification with a 14.3% increase in F1-score compared to the baseline method.
dc.identifier.citation2023 20th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2023, 2023
dc.identifier.doi10.1109/ECTI-CON58255.2023.10153191
dc.identifier.other2-s2.0-85164922191
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13987
dc.source2023 20th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2023
dc.subjectDeep Learning
dc.subjectRoad Traffic Incident
dc.subjectText augmentation
dc.subjectText Classification
dc.subjectTwitter Data Analytics
dc.titleImproving a text classifier using text augmentation: road traffic content from Twitter
dc.typeConference Paper

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