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. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons. 1
Researchers realize the importance of the target, Tang et al. REF build two target-dependent LSTM network to model the right and left contexts with the target to make use of target information in the sentiment polarity detection.
Effective LSTMs for Target-Dependent Sentiment Classification
COLING 2016
arXiv: Computation and Language
Computer Science