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

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

研究成果: 書貢獻/報告類型會議貢獻

摘要

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.
原文英語
主出版物標題Proceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015
發行者Institute of Electrical and Electronics Engineers Inc.
頁面569-573
頁數5
ISBN(電子)9781467366564
DOIs
出版狀態已發佈 - 十月 19 2015
對外發佈
事件16th IEEE International Conference on Information Reuse and Integration, IRI 2015 - San Francisco, 美国
持續時間: 八月 13 2015八月 15 2015

會議

會議16th IEEE International Conference on Information Reuse and Integration, IRI 2015
國家/地區美国
城市San Francisco
期間8/13/158/15/15

ASJC Scopus subject areas

  • 資訊系統
  • 資訊系統與管理
  • 電氣與電子工程

指紋

深入研究「Neural Network-Based Vector Representation of Documents for Reader-Emotion Categorization」主題。共同形成了獨特的指紋。

引用此