Computer Science ›› 2019, Vol. 46 ›› Issue (7): 151-156.doi: 10.11896/j.issn.1002-137X.2019.07.024

• Artificial Intelligence • Previous Articles     Next Articles

Sentiment Classification Towards Question-Answering Based on Bidirectional Attention Mechanism

SHEN Chen-lin,ZHANG Lu,WU Liang-qing,LI Shou-shan   

  1. (School of Computer Science & Technology,Soochow University,Suzhou,Jiangsu 215006,China)
  • Received:2018-06-12 Online:2019-07-15 Published:2019-07-15

Abstract: Sentiment classification is a fundamental task in natural language processing,which aims at inferring the sentiment polarity of a given text.Previous studies for sentiment classification,mainly focus on sentence,document and tweet text styles.Different from these researches,this paper focused on a novel text style,i.e.,question-answering (QA) review,for sentiment classification.Firstly,a large-scale and high-quality QA review corpus was collected and built.Then,a bidirectional attention neural network for QA sentiment classification was proposed.Specifically,the question and answer text with Bi-LSTM were encoded respectively.After that,sentiment weights in question and answer text were calculated synchronously by employing bidirectional attention mechanism.Finally,the sentiment matching representation for each QA review with sentiment weights can be obtained.Empirical studies show that the proposed approach achieves a great result (75.5% in Accuracy and 61.4% in Macro F1),and has remarkable improvement compared with other baselines.

Key words: Sentiment classification, Attention mechanism, Question-Answering

CLC Number: 

  • TP391
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