Aspect-based sentiment analysis neural network using pre-trained language model

DOI

Bibliographic Information

Other Title
  • 事前学習言語モデルを用いたアスペクトベースセンチメント分析ニューラルネットワーク
  • Estimation of Multiple aspect category polarities and target phrases

Abstract

<p>Sentiment analysis is a task that aims to analyze opinions, feelings, and attitudes from texts, and classifies whether the polarity of them is positive or negative. One of the tasks of sentiment analysis is aspect-based sentiment analysis. This task analyzes the sentiment of a text by extracting entities and attributes as aspectual information contained in the text, and classifies the polarity of them from their context. In this paper, we propose a neural network model that solves three tasks of identification of multiple aspect categories, polarity classification, and identification of target phrases for each aspect category by using the pre-trained language model BERT for text encoding. The performance of the model is evaluated using the SemEval dataset. Experiments show that the accuracy of the model in identifying aspect categories in texts and estimating their polarity is 98% and 95% respectively and the accuracy of the target phrase estimation is 81%.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390851320457534720
  • NII Article ID
    130008051723
  • DOI
    10.11517/pjsai.jsai2021.0_2yin507
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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