Table 4.

Classification accuracy comparison for domain adaptation methods for sentiment analysis (positive or negative review) on the Amazon review dataset [18]^a with domains books (B), DVD (D), electronics (E), and kitchen (K). Adversarial approaches denoted by *.

Source→Target	DANN[73]b*	DANN[73]c*	CORAL[225]d	ATT[204]c	WDGRL[217]ce*	No Adapt.[225]f
B→D	82.9	78.4		80.7	83.1
B→E	80.4	73.3	76.3	79.8	83.3	74.7
B→K	84.3	77.9		82.5	85.5
D→B	82.5	72.3	78.3	73.2	80.7	76.9
D→E	80.9	75.4		77.0	83.6
D→K	84.9	78.3		82.5	86.2
E→B	77.4	71.3		73.2	77.2
E→D	78.1	73.8		72.9	78.3
E→K	88.1	85.4	83.6	86.9	88.2	82.8
K→B	71.8	70.9		72.5	77.2
K→D	78.9	74.0	73.9	74.9	79.9	72.2
K→E	85.6	84.3		84.6	86.3

^ahttp://www.cs.jhu.edu/~mdredze/datasets/sentiment/

^busing 30,000-dimensional feature vectors from marginalized stacked denoising autoencoders (mSDA) by Chen et al. [36], which is an unsupervised method of learning a feature representation from the training data

^cusing 5000-dimensional unigram and bigram feature vectors

^dusing bag-of-words feature vectors including only the top 400 words, but suggest using deep text features in future work

^ethe best results on target data for various hyperparameters

^fusing bag-of-words feature vectors