Multi-Target Stance Detection Based on GRU-PWV-CNN Network Model

To uncover opinions on different people and events from text on the internet, stance detection must be performed, which requires an algorithm to mine stance tags for different targets (people or events). Some text contain multiple targets, and the content describing different targets is related, which results in poor stance detection performances. Therefore, stance detection for such data is defined as multi-target stance detection. To address this issues, a network model composed of a gated recurrent unit, a position weight vector, and a convolutional neural network (GRU-PWV-CNN) is proposed. First, the bidirectional GRU (Bi-GRU) is employed to extract the unstructured features, and a position-weight vector is designed to represent the correlation between every word and the given target. Next, these two forms of information are fused and transmitted to a CNN to complete the secondary extraction of features. Finally, a softmax function is used to carry out the final classification. A multi-target stance detection corpus for the 2016 US election was used to compare the performances of our method and other methods, including the Seq2Seq and AH-LSTM. The experimental results showed that the proposed method achieved well and had a 2.82% improvement in the macro-averaged F1-score.