Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments
Table 3. F1-score, Precision, and Recall of each method
['[BOLD] (a) TV-series Baselines', '[BOLD] (a) TV-series Precision', '[BOLD] (a) TV-series Recall', '[BOLD] (a) TV-series F1-score']
The experiments of all the models are repeated for 10 times, and we use the average values as the final results. Compared to the-state-of-art DN-GAA method proposed by Chang et al In other baselines, keyword-matching (KM) method achieves high Recall and low Precision, since it treats many non-spoiler TSCs as spoilers. Latent Dirichlet Allocation (LDA) based method does not perform well because the LDA model is not suitable to process short texts like TSC data. LI-NPP is an SVM-based method and has better performance than the unsupervised learning methods. DN-GAA is the state-of-the-art method and has the highest performance among the baselines. The results show that the neural network has powerful feature extraction capabilities when there is sufficient data.