Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
TABLE II: Comparison of different methods on Restaurant and Hotel data sets. Evaluation metrics are accuracy and macro-F1 score. Best scores are in bold.
['[BOLD] Model', '[BOLD] Restaurant  [BOLD] ACC', '[BOLD] Restaurant  [BOLD] Macro-F1', '[BOLD] Hotel  [BOLD] ACC', '[BOLD] Hotel  [BOLD] Macro-F1']
Our MAN models obtain substantial accuracy and macro-F1 improvements, which demonstrate the power of techniques applied in our model. Compared with models without any attention mechanism, MAN improves performance through the multiple-attention mechanisms. With this design, our model can learn the representations of vital words contributing to aspect-level and overall sentiment classifications. As the two data sets are highly imbalanced that there are much more positive reviews than the negative ones Therefore, the great improvement of the macro-F1 score demonstrates that our MAN models are very effective.