@inproceedings{6954d617e75641828accf8964018e630,
title = "Neural Network-Based Vector Representation of Documents for Reader-Emotion Categorization",
abstract = "In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures among words, sentences, and even documents. Various methods of combining the word embeddings are tested for their performances on reader-emotion categorization of a Chinese news corpus. Results demonstrate that the proposed method, when compared to several other approaches, can achieve comparable or even better performances.",
keywords = "document representation, neural network, reader emotion, word embedding",
author = "Hsieh, {Yu Lun} and Liu, {Shih Hung} and Chang, {Yung Chun} and Hsu, {Wen Lian}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 16th IEEE International Conference on Information Reuse and Integration, IRI 2015 ; Conference date: 13-08-2015 Through 15-08-2015",
year = "2015",
month = oct,
day = "19",
doi = "10.1109/IRI.2015.90",
language = "English",
series = "Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "569--573",
booktitle = "Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015",
address = "United States",
}