@inproceedings{ee7e7a929e2d4df78ea17250d87141fe,
title = "Sentiment classification using neural networks with sentiment centroids",
abstract = "Neural networks (NN) have demonstrated powerful ability to extract text features automatically for sentiment classification in recent years. Although semantic and syntactic features are well studied, global category information has been mostly ignored within the NN based framework. Samples with the same sentiment category should have similar vectors in represent space. Motivated by this, we propose a novel global sentiment centroids based neural framework, which incorporates the sentiment category features. The centroids assist NN to extract discriminative category features from a global perspective. We apply our approach to several real large-scale sentiment-labeled datasets, and the extensive experiments show that our model not only obtains more powerful sentiment feature representations, but also achieves some state-of-the-art results with a simple neural network structure.",
keywords = "Deep neural network, Sentiment centroids, Sentiment classification",
author = "Maoquan Wang and Shiyun Chen and Liang He",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 06-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93034-3\_5",
language = "英语",
isbn = "9783319930336",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "56--67",
editor = "Dinh Phung and Webb, \{Geoffrey I.\} and Bao Ho and Tseng, \{Vincent S.\} and Mohadeseh Ganji and Lida Rashidi",
booktitle = "Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings",
address = "德国",
}