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Aspect Based Sentiment Analysis for Online Reviews

  • Lamei Xu
  • Jin Liu
  • Lina Wang
  • Chunyong Yin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

Learning good semantic vector representations for sentiment analysis in phrases, sentences and paragraphs is a challenging and ongoing area of natural language processing. In this paper, we propose a Convolution Neural Network for aspect level sentiment classification. Our model first builds a convolution neural network model to aspect extraction. Afterwards, we used a sequence labeling approach with Conditional Random Fields for the opinion target detection. Finally, we concatenate an aspect vector with every word embedding and apply a convolution neural network over it to determine the sentiment towards an aspect. Results of an experiment show that our method performs comparably well on Yelp reviews.

Keywords

Convolution neural network Conditional random fields Word2-ec Aspect-based sentiment analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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