The rise of social media has led to the increasing of comments on online forums. However, there still exists some invalid comments which were not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for classifying constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we proposed a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we achieved 78.59\% and 59.40\% F1-score for identifying constructive and toxic comments separately . Besides, to have an objective assessment for the dataset, we implement a variety of baseline models as traditional Machine Learning and Deep Neural Network-Based models  . With the results, we can solve some problems on the online discussions and develop the framework for identifying constructiveness and toxicity  Vietnamese social media comments automatically.
The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59\% and 59.40\% for classifying constructive and toxic comments , respectively . Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset . With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
D
some
R
were
are
D
classifying
R
proposed
propose
R
achieved
obtain F1-scores of
R
F1-score for identifying
for classifying
R
separately
, respectively
R
to have an objective assessment for the dataset, we implement a variety of
we implement various
A
to evaluate the dataset
R
some problems
several tasks
A
of