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A Target-Dependent Sentiment Analysis Method for Micro-blog Streams

  • Yongheng WangEmail author
  • Hui Gao
  • Shaofeng Geng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

Sentiment analysis technique is useful for companies to analyze customer’s opinion about products and to find potential customers. Most of the target-dependent sentiment analysis methods can not get acceptable accuracy. Recently some new sentiment analysis methods using Recursive Neural Networks (RNN) are promissing but they are not target-dependent. In this paper we propose a target-dependent sentiment analysis method for micro-blog streams based on RNN. We use cluster-based data partitioning to get higher accuracy with limited labeled samples. A tree pruning method is proposed to remove irrelevant parts from the syntax tree. The original recursive neural network model is extended to support target-dependent sentiment analysis better. Experimental results on two corpuses with different targets show that the performance of our method is better than previous methods.

Keywords

Sentiment Analysis Domain Ontology Formal Concept Analysis Tree Pruning Syntax Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHunan UniversityChangshaChina

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