A novel deep fully convolutional encoder-decoder network and similarity analysis for English education text event clustering analysis

Abstract

Education event clustering for social media aims to achieve short text clustering according to event characteristics in online social networks. Traditional text event clustering has the problem of poor classification results and large computation. Therefore, we propose a novel deep fully convolutional encoder-decoder network and similarity analysis for English education text event clustering analysis in online social networks. At the encoder end, the features of text events are extracted step by step through the convolution operation of the convolution layer. The background noise is suppressed layer by layer while the target feature representation is obtained. The decoder end and the encoder end are symmetrical in structure. In the decoder end, the high-level feature representation obtained by the encoder end is deconvolved and up-sampled to recover the target event layer by layer. Based on the linear model, text similarity is calculated and incremental clustering is performed. In order to verify the effectiveness of the English education text event analysis method based on the proposed approach, it is compared with other advanced methods. Experiments show that the performance of the proposed method is better than that of the benchmark model.