Negative comment recognition model based on lightGBM

Abstract

With the popularization and rapid development of the Internet, the negative impact caused by negative comments is exacerbated. The increasingly common negative comments on various video websites and social networking sites have exercised a malign influence on public opinion and caused adverse social consequences. This paper uses LightGBM to establish a high-performance negative comment prediction model. The data set selected in this paper is from the Internet movie database IMDB, in which positive and negative comments have been marked for our training model. After Bag of Words processing and TF-IDF processing of the dataset and substituting it into our training model, the researchers obtained the final model with an accuracy of more than 95%, which has a more satisfactory performance compared with the classical integrated model and the traditional binary classification model. Thus it can be seen that this model has an advantage in identifying spiteful comments online.

Assessing the relationship between landslide susceptibility and land cover change using machine learning

“…Additionally, this model performs well on the large dataset (Qiu, Wang, & Li, 2023;T. Zhang et al, 2023).…”