Neural Network-Based Vector Representation of Documents for Reader-Emotion Categorization

Yu Lun Hsieh, Shih Hung Liu, Yung Chun Chang, Wen Lian Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages569-573
Number of pages5
ISBN (Electronic)9781467366564
DOIs
Publication statusPublished - Oct 19 2015
Externally publishedYes
Event16th IEEE International Conference on Information Reuse and Integration, IRI 2015 - San Francisco, United States
Duration: Aug 13 2015Aug 15 2015

Conference

Conference16th IEEE International Conference on Information Reuse and Integration, IRI 2015
CountryUnited States
CitySan Francisco
Period8/13/158/15/15

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Keywords

  • document representation
  • neural network
  • reader emotion
  • word embedding

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Electrical and Electronic Engineering

Cite this

Hsieh, Y. L., Liu, S. H., Chang, Y. C., & Hsu, W. L. (2015). Neural Network-Based Vector Representation of Documents for Reader-Emotion Categorization. In Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015 (pp. 569-573). [7301028] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2015.90