@inproceedings{8b96218e9f124e35af9e6c55107b058a,
title = "SG++: Word representation with sentiment and negation for twitter sentiment classification",
abstract = "Here we propose an advance Skip-gram model to incorporate both word sentiment and negation information. In particular, there is aa softmax layer for the word sentiment polarity upon the Skip-gram model. Then, two paralleled embedding layers are set up in the same embedding space, one for the affirmative context and the other for the negated context, followed by their loss functions. We evaluate our proposed model on the 2013 and 2014 SemEval data sets. The experimental results show that the proposed approach achieves better performance and learns higher dimensional word embedding informatively on the large-scale data.",
keywords = "Negation, Neural network, Twitter sentiment classification, Word representation",
author = "Qinmin Hu and Yijun Pei and Qin Chen and Liang He",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 ; Conference date: 17-07-2016 Through 21-07-2016",
year = "2016",
month = jul,
day = "7",
doi = "10.1145/2911451.2914718",
language = "英语",
series = "SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery, Inc",
pages = "997--1000",
booktitle = "SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}