Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
Table 2: Comparison of our approaches and other baseline approaches to DASC. The results with symbol † are retrieved from DBLP:conf/coling/LiYZ18 and those with ‡ are from DBLP:conf/emnlp/YinSZ17
['[BOLD] Approaches', '[BOLD] TripUser  [ITALIC] Development', '[BOLD] TripUser  [ITALIC] Development', '[BOLD] TripUser  [ITALIC] Test', '[BOLD] TripUser  [ITALIC] Test', '[BOLD] TripAdvisor  [ITALIC] Development', '[BOLD] TripAdvisor  [ITALIC] Development', '[BOLD] TripAdvisor  [ITALIC] Test', '[BOLD] TripAdvisor  [ITALIC] Test', '[BOLD] BeerAdvocate  [ITALIC] Development', '[BOLD] BeerAdvocate  [ITALIC] Development', '[BOLD] BeerAdvocate  [ITALIC] Test', '[BOLD] BeerAdvocate  [ITALIC] Test']
From this table, we can see that, all LSTM-based approaches outperform SVM, showing that LSTM has potentials in automatically learning text representations and can bring performance improvement for DASC.