Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 93-97.

• Intelligent Computing • Previous Articles     Next Articles

Emotion Classification Algorithm Based on Emotion-specific Word Embedding

ZHANG Lu, SHEN Chen-lin, LI Shou-shan   

  1. School of Computer Science & Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Emotion analysis is a hot research issue in the field of NLP,and it infersthe feelings of individuals through analyzing the text they have published.Emotion classification is a fundamental task in emotion analysis,which aims to determine the emotion categories in a piece of text.The representation of words is a critical prerequisite for emotion classification.Many intuitive choices of learning word embedding are available,but these word embedding algorithms typically model the syntactic context of words but ignore the emotion information relevant to words.As a result,words with opposite emotion but similar syntactic context tend to be represented as close vectors.To address the problem,this paper proposeda a heterogeneous network composed of two basic networks,i.e.,document-word network and emoticon-word network to learn emotion-specific word embedding .Finally,an LSTM network was trained on the labeled data.Empirical studies demonstrate the effectiveness of the proposed approach to learn emotion-specific word embedding.

Key words: Sentiment analysis, Emotion classification, Word Embedding

CLC Number: 

  • TP391
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