Learning Domain-Sensitive and Sentiment-Aware Word Embeddings This work was partially done when Bei Shi was an intern at Tencent AI Lab. This project is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14203414).
Table 1: Results of review sentiment classification. The markers † and ♮ refer to p-value < 0.05 when comparing with Yang and SSWE respectively.
['[EMPTY]', 'B & D  [ITALIC] Acc.', 'B & D F1', 'B & E  [ITALIC] Acc.', 'B & E F1', 'B & K  [ITALIC] Acc.', 'B & K F1', 'D & E  [ITALIC] Acc.', 'D & E F1', 'D & K  [ITALIC] Acc.', 'D & K F1', 'E & K  [ITALIC] Acc.', 'E & K F1']
We conduct multiple trials by selecting every possible two domains from books (B), DVDs (D), electronic items (E) and kitchen appliances (K). We use the average of the results of each two domains. Our statistical t-tests for most of the combinations of domains show that the improvement of our DSEw model over Yang and SSWE is statistically significant respectively (p-value <0.05) at 95% confidence level. It shows that our method can capture the domain-commonality and sentiment information at the same time.