Blog text quality assessment using a 3D CNN-based statistical framework

Aiming at the problem that blog texts are the streaming data captured by different acquisition modality, each kind of which has its particular quality evaluation mode, this paper proposes a text quality evaluation (TQA) model based on 3D CNN correlated with blog text data. In order to achieve accurate TQA value, the model adopted a Bi-LSTM-based architecture to process video-related blog text as auxiliary part to provide additional information for our TQA architecture. First, the auxiliary part constructs feature vector for each video-related text by the model originating from Bi-LSTM and Seq2Seq. Then, the feature vector was feed to a well-trained decoder to reconstruct the original input data. Then, the feature vector complied with the blog textual data are inputted into end-to end TQA modal based on the 3D CNN straightly. Comprehensive experimental results on the blog text/video dataset from the well-known truism website "http://www.mafengwo.cn/"have shown that the proposed model reflects the subjective quality of online texts more accurately, and has better overall blog TQA assessment performance than the other state-of-the-art non-reference methods. (C) 2020 Published by Elsevier B.V.