Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees
Table 6: Sensitivity analysis of how the discourse parser affects the predictive performance of our Discourse-LSTM. Here a varying proportion of relation types are replaced by random noise. The results originate from the Amazon Fine Food dataset and an N-ary Discourse-LSTM.
['[BOLD] Percentage of noisy', '[BOLD] Balanced accuracy', '[BOLD] F1-score']
When modifying 2\char37 of the relation types, we see no difference in terms of balanced accuracy, but a decrease of 0.007 points in the F1-score. By altering 10\char37 of the relation types, the balanced accuracy decreases to 0.803 with an F1-score of 0.794. This reduction is statistically significant at the 1\char37 level. When modifying 20\char37 of the relation types, the performance decreases further to a balanced accuracy of 0.794 and an F1-score of 0.788.