Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Table 5: Jensen-Shannon divergence between term distribution representations of datasets. The bold numbers represent the most similar domains and underlined numbers represent the most divergent.
['[EMPTY]', 'book', 'DVD', 'electronics', 'kitchen', 'SemEval 2013', 'SemEval 2016']
For each domain, we create frequency distributions of the most frequent 10,000 unigrams that all domains have in common and measure the divergence with DJS. This is especially true for the distance between the SemEval datasets from the kitchen dataset (DJS = 0.741 and 0.761, respectively). This suggests that Nscl and mSDA give the best results when the difference between domains is relatively small, whereas Blse performs better on more divergent datasets.