Sentiment Strength Prediction Using Auxiliary Features

Author:

Chen Huijun1,Li Xin1,Rao Yanghui1,Xie Haoran2,Wang Fu Lee3,Wong Tak-Lam2

Affiliation:

1. Sun Yat-sen University, Guangzhou, China

2. The Education University of Hong Kong, Hong Kong SAR, China

3. Caritas Institute of Higher Education, Hong Kong SAR, China

Funder

Internal Research Grant of The Education University of Hong Kong

Start-Up Research Grant of The Education University of Hong Kong

Research Grants Council of the Hong Kong Special Administrative Region China

Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund

National Natural Science Foundation of China

Publisher

ACM Press

Reference43 articles.

1. R. Astudillo, S. Amir, W. Lin, M. Silva, and I. Trancoso. Learning word representations from scarce and noisy data with embedding sub-spaces. In ACL, pages 1074--1084, 2015.

2. S. Baccianella, A. Esuli, and F. Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC, pages 2200--2204, 2010.

3. Y. Bengio, H. Schwenk, J. Senécal, F. Morin, and J. Gauvain. Neural probabilistic language models. J. Mach. Learn. Res., 3(6):1137--1155, 2003.

4. D. M. Blei. Probabilistic topic models. ACM Commun., 55(4):77--84, 2012.

5. X. Chen, L. Xu, Z. Liu, M. Sun, and H. Luan. Joint learning of character and word embeddings. In IJCAI, pages 1236--1242, 2015.

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