Classification of comments on social media based on long short-term memory

Abstract

Social media has assumed a pivotal role in contemporary society, significantly enhancing the convenience of daily lives. Nonetheless, the prevalence of toxic comments on social media platforms has led to varying degrees of harm for individuals. The conventional practice of manually categorizing and blocking such toxic comments has proven to be highly inefficient. To address this issue, this study employs artificial intelligence natural language processing technology to classify social media comments, offering a more effective solution. In the past few years, many algorithms for handling text classification tasks have been introduced and applied in various scenarios. In this work, the author used an LSTM model that can effectively handle long sequence dependency problems to implement text classification. This study achieved an accuracy of 99.4% after training on the Kaggle toxic comments datasets. During the training process, the training accuracy is greater than the validation accuracy while the validation loss is lower than the training loss. After training, the trained model can accurately predict an input sentence and the results are within the expected range.