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RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11775 LNAI, Page: 667-675
2019
  • 13
    Citations
  • 0
    Usage
  • 6
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    13
    • Citation Indexes
      13
  • Captures
    6

Conference Paper Description

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we propose to use a novel framework named as recurrent graph convolutional network (RGCN) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines.

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