融合词性和注意力的卷积神经网络对象级情感分类方法

多个对象同时讨论时,对文本的情感分析结果与针对特定对象的情感倾向可能不一致,对象级情感分类任务需在文本整体语义的场景下,重点关注与给定对象相关的内容.文中提出融合词性和注意力的卷积神经网络对象级情感分类方法.引入词性信息,通过长短时记忆神经网络建模输入序列,构建对象注意力,将注意力融入到卷积神经网络结构中分析关于给定对象的情感倾向.词性信息有助于捕获与对象具有修饰关系的内容和弱化内容或距离相近但无搭配关系的句子成分的影响.结合长短时记忆神经网络和卷积神经网络结构建模文本,更有利于同时建模文本整体语义与对象相关语义.在SemEval2014数据集上的实验表明,文中方法取得优于基于长短时记忆神经网络的注意力机制方法的分类效果.

CNN with Part-of-Speech and Attention Mechanism for Targeted Sentiment Classification

Targets are usually discussed together. Sentiment towards the given target may be different from the sentiment polarity of the whole text. It is necessary to focus on the related context to the target in the whole semantic scenario for targeted sentiment analysis tasks. This paper presents a targeted sentiment classification method based on convolutional neural network(CNN) with Part-of-Speech(POS) and attention mechanism. POS information is introduced into the model as a supplement to text features. Attention mechanism with respect to the given target is built based on long short term memory neural network(LSTM) modeling of the input sequence. Then, the relevant parts to the target of the input text are enhanced according to the attention and the modified sequence is input to CNN sentiment classification structure to analyze the polarity towards the given target. POS information helps to capture the context with collocation relation to the target, which will help to reduce the influence of the context with similar content or short distance but no collocation relation. LSTM and CNN modeling the input text together can be beneficial to capture semantics of the whole text and those towards the given target at the same time effectively. Experiments on SemEval2014 dataset shows the effectiveness of the model compared to attention methods based on LSTM.