基于注意力机制的文本情感倾向性研究

社交媒体上短文本情感倾向性分析作为情感分析的一个重要分支,受到越来越多研究人员的关注。为了改善短文本特定目标情感分类准确率,提出了词性注意力机制和LSTM相结合的网络模型PAT-LSTM。将文本和特定目标映射为一定阈值范围内的向量,同时用词性标注处理句子中的每个词,文本向量、词性标注向量和特定目标向量作为模型的输入。PAT-LSTM可以充分挖掘句子中的情感目标词和情感极性词之间的关系,不需要对句子进行句法分析,且不依赖情感词典等外部知识。在SemEval2014-Task4数据集上的实验结果表明,在基于注意力机制的情感分类问题上,PAT-LSTM比其他模型具有更高的准确率。

Text sentiment analysis based on attention mechanism

As an important branch of sentiment analysis, short-text sentiment classification on social media has attracted more and more researchers’ attention. To improve the accuracy of the short text target based sentiment classification, we propose a network model that combines the part-of-speech attention mechanism with long short-term memory (PAT-LSTM). The text and the target are mapped to a vector within a certain threshold range. In addition, each word in the sentence is marked by the part-of-speech. The text vector, target vector and part-of-speech vector are then input into the model. The PAT-LSTM model can fully explore the relationship between target words and emotional words in a sentence, and it does not require syntactic analysis of sentences or external knowledge such as sentiment lexicon. The results of comparative experiments on the Eval2014 Task4 dataset show that the PAT-LSTM network model has higher accuracy in attention-based sentiment classification.