Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
Table 4: Experimental results on given datasets
['Method', 'Negative class (%) precision', 'Negative class (%) recall', 'Negative class (%) F1', 'Positive class (%) precision', 'Positive class (%) recall', 'Positive class (%) F1', 'Overall (%) accuracy', 'Overall (%) F1']
We compare performance of the proposed method to support vector machine and convolutional neural network for short sentences by using pre-trained Google word embeddings (kim2014convolutional). It is important to note how well an algorithm is performing on different classes in a dataset, for example, SVM is not showing good performance on positive samples of Stanford dataset which is probably due to the sample size and therefore the model is biased toward the negative class. On the other hand, F1 scores of the proposed method for both positive and negative classes show how efficiently the algorithm can extract features from different classes and do not get biased toward one of them.