基于极性转移和LSTM递归网络的情感分析

Polarity Shifting and LSTM Based Recursive Networks for Sentiment Analysis

长短时记忆(long short term memory,LSTM)是一种有效的链式循环神经网络(recurrent neural network,R2 NN1),被广泛用于语言模型、机器翻译、语音识别等领域。但由于该网络结构是一种链式结构,不能有效表征语言的结构层次信息,该文将LSTM扩展到基于树结构的递归神经网络(Recursive Neural Network,RNN)上,用于捕获文本更深层次的语义语法信息,并根据句子前后词语间的关联性引入情感极性转移模型。实验证明本文提出的模型优于LSTM、递归神经网络等。

The chain-structured long shortterm memory （LSTM） has been shown to be effective in a wide range of tasks such as language modeling, machine translation and speech recognition. Because it cannot storage the structure of hierarchical information language, we extend it to a tree-structure based recursive neural network to capture more syntactic and semantic information, as well as the sentiment polarity shifting. Compared to LSTM, RNN ete, the proposed model achieves a state-of-the-art performance.