Rethinking Attribute Representation and Injectionfor Sentiment Classification
Table 2: Sentiment classification results of competing models based on accuracy and RMSE metrics on the three datasets. Underlined values correspond to the best values for each block. Boldfaced values correspond to the best values across the board. 1uses additional external features, 2uses a method that considers cold-start entities, 3uses separate bias-attention for user and product.
['Model', 'Model', 'Model', 'IMDB', 'IMDB', 'Yelp 2013', 'Yelp 2013', 'Yelp 2014', 'Yelp 2014']
On all three datasets, our best results outperform all previous models based on accuracy and RMSE. Among our four models, CHIM-embedding performs the best in terms of accuracy, with performance increases of 2.4%, 1.3%, and 1.6% on IMDB, Yelp 2013, and Yelp 2014, respectively. CHIM-classifier performs the best in terms of RMSE, outperforming all other models on both Yelp 2013 and 2014 datasets. Among our models, CHIM-attention mechanism performs the worst, which shows similar results to our previous experiment We emphasize that our models use a simple BiLSTM as base model, and extensions to the base model (e.g., using multiple hierarchical LSTMs as in Wu et al. Amplayo et al. Thus, we expect a further increase in performance when these extensions are done.