Effective LSTMs for Target-Dependent Sentiment Classification

Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well.…

• NN
  National Natural Science Foundation of China

  Awards: 61273321, 61632011, 863 Program, 2015AA015407, and No.
• HI
  Harbin Institute of Technology
• NH
  National High-tech Research and Development Program