TITLE: Using GAN-based models to sentimental analysis on imbalanced datasets in education domain 

ABSTRACT: While the whole world is still struggling with the COVID-19 pandemic, online
learning and home office become more common. Many schools transfer their
courses teaching to the online classroom. Therefore, it is significant to mine
the students' feedback and opinions from their reviews towards studies so that
both schools and teachers can know where they need to improve. This paper
trains machine learning and deep learning models using both balanced and
imbalanced datasets for sentiment classification. Two SOTA category-aware text
generation GAN models: CatGAN and SentiGAN, are utilized to synthesize text
used to balance the highly imbalanced dataset. Results on three datasets with
different imbalance degree from distinct domains show that when using generated
text to balance the dataset, the F1-score of machine learning and deep learning
model on sentiment classification increases 2.79% ~ 9.28%. Also, the results
indicate that the average growth degree for CR100k is higher than CR23k, the
average growth degree for deep learning is more increased than machine learning
algorithms, and the average growth degree for more complex deep learning models
is more increased than simpler deep learning models in experiments.
While the whole world is still struggling with the COVID-19 pandemic, online
learning and home office become more common. Many schools transfer their
courses teaching to the online classroom. Therefore, it is significant to mine
the students' feedback and opinions from their reviews towards studies so that
both schools and teachers can know where they need to improve. This paper
trains machine learning and deep learning models using both balanced and
imbalanced datasets for sentiment classification. Two SOTA category-aware text
generation GAN models: CatGAN and SentiGAN, are utilized to synthesize text
used to balance the highly imbalanced dataset. Results on three datasets with
different imbalance degree from distinct domains show that when using generated
text to balance the dataset, the F1-score of machine learning and deep learning
model on sentiment classification increases 2.79% ~ 9.28%. Also, the results
indicate that the average growth degree for CR100k is higher than CR23k, the
average growth degree for deep learning is more increased than machine learning
algorithms, and the average growth degree for more complex deep learning models
is more increased than simpler deep learning models in experiments.
Using GAN-based models to sentimental analysis on imbalanced datasets in education domain
http://arxiv.org/abs/2108.12061v1