Chawuthai, Rathachai

Loading...
Thumbnail Image
Preferred name
Chawuthai, Rathachai
Alternative Name
Chawuthai, R.
Main Affiliation
Email
rathachai.ch@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Classification Model for Road Traffic Incidents on Twitter Data
    (2022-01-01)
    Raksachat, Thawatchai
    ;
    Chawuthai, Rathachai
    This study aims to create a classification model for road traffic incidents in Thailand using Twitter data. The challenging issue of our work is to deal with highly imbalanced dataset of 5 classes. As we surveyed, some pieces of research solved this issue by the Markov Chains method. However, using the Markov Chains in our dataset provides low performance, so we study the Undersampling, Oversampling, Markov Chains, and Bi-directional Long Short-Term Memory (Bi-LSTM). As we use the Markov Chains as the baseline, the result of our experiment found that using Bi-LSTM provides the improvement of F1-score up to 15.44% against the baseline.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Improving a text classifier using text augmentation: road traffic content from Twitter
    (2023-01-01)
    Raksachat, Thawatchai
    ;
    Chawuthai, Rathachai
    The 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.