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 2: Results of lexicon term sentiment classification.
['[EMPTY]', 'B & D HL', 'B & D MPQA', 'B & E HL', 'B & E MPQA', 'B & K HL', 'B & K MPQA', 'D & E HL', 'D & E MPQA', 'D & K HL', 'D & K MPQA', 'E & K HL', 'E & K MPQA']
Our DSE method can achieve competitive performance among all the methods. Compared with SSWE, our DSE is still competitive because both of them consider the sentiment information in the embeddings. Our DSE model outperforms other methods which do not consider sentiments such as Yang, EmbeddingCat and EmbeddingAll. Note that the advantage of domain-sensitive embeddings would be insufficient for this task because the sentiment lexicons are not domain-specific.