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Semantic sentiment analysis based on a combination of CNN and LSTM model

With the development of social media, there are more and more platforms for people to express their opinions and ideas openly, so there are more and more comments on the same thing and event from different angles on the Internet. Therefore, in order to clean up the internet environment, it is important to root out the source of toxic comments in each other’s anonymous web surfing. In this paper, we proposed a model which combines convolutional neural network (CNN) and long and short-term memory network (LSTM). We used a dataset from Kaggle Toxic comment challenge for model training and testing. Finally, the experimental results that ROC AUC is 0.98 which outperforms CNN and LSTM individually used as a classifier for the task.