The Risk Level Estimation Based on Deep Learning Method for Tianya Forum

Using the societal risk indicators from socio psychology, a deep learning method is applied to estimate the risk level of Tianya Forum. Due to the effectiveness in semantic and word order information extraction for documents, a deep learning method Post Vector is used to generate the distributed representations of BBS posts. Through the experimental comparison on societal risk classification of BBS posts, the performance of kNN based on Post Vector is superior to kNN based on Bag-of-Words, edit distance or Lucene-based search method. Therefore, with kNN based on Post Vector method and the annotated data of Tianya Zatan broad, the risk level of Baixing Shengyin broad in different months is estimated, and the reasonability of the estimated results is analyzed.