With the transformed word embedding feature, SVM can achieve comparable performance as SVM with n-gram feature.
here we apply three convolutional layers on the concatenated transformed word embeddings  xc w is the word embedding of word w in the comments; U k and u k are the moderator matrix and vector embedding for moderator k; T j and t j are the topic matrix and vector embedding for topic j; R i and r i are the commenter matrix and vector embedding for commenter i.