Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
Table 6: Comparison of different CNN models on the sampled data with graph embeddings
['Method', 'Negative class (%) precision', 'Negative class (%) recall', 'Negative class (%) F1', 'Positive class (%) precision', 'Positive class (%) recall', 'Positive class (%) F1', 'Overall (%) accuracy', 'Overall (%) F1']
We demonstrate the performance of the above described models on the sampled data from all available datasets (250 negative and 250 positive documents divided into train and test on 80-20 ratio) to figure out which model is the best choice to be coupled with graph embeddings. The results reveal that the CNN-static model is close and at some levels is better than CNN-non-static. Moreover, this indicates that the feature set extracted from the graphs are rich enough and don’t require optimization and fine-tuning.