Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications
Table 5: Comparison of graph-based learning vs. word2vec
['Method', 'Negative class (%) precision', 'Negative class (%) recall', 'Negative class (%) F1', 'Positive class (%) precision', 'Positive class (%) recall', 'Positive class (%) F1', 'Overall (%) accuracy', 'Overall (%) F1']
With the intention to show the priority of the graph representation procedure over word2vec, we have extracted the word embeddings only on the IMDB dataset to demonstrate the effect of graph representation on text documents. This shows the superiority of the graphs in extracting features from the text materials even if the corpus size is limited. It is worth mentioning that the word graphs are made only out of the available corpus and are not dependent on any external features.