基于情感评分的分层文本表示情感分类方法

文本中的词并非都具有相似的情感倾向和强度,较好地编码上下文并从中提取关键信息对于情感分类任务而言非常重要。为此,提出一种基于情感评分的分层注意力网络框架,以对文本情感进行有效分类。利用双向循环神经网络编码器分别对词向量和句向量进行编码,并通过注意力机制加权求和以获得文档的最终表示。设计辅助网络对文本的词、句进行情感评分,利用该评分调整注意力权重分布。在探究文本的情感信息对分类性能的影响后,通过辅助网络进一步促使模型关注情感色彩强烈的信息。在4个常用情感分类数据集上的实验结果表明,该框架能够关注文本中的情感表达并获得较高的分类准确率。

Sentiment Evaluation Based Hierarchical Text Representation Methodfor Sentiment Classification

Not all words in the text have similar sentiment tendency and intensity,so it is very important for sentiment classification tasks that the context is well encoded and the key information is extracted.Therefore,this paper proposes a hierarchical attention network framework based on sentiment evaluation to conduct effective classification for text sentiment.The bidirectional recurrent neural network encoder is used to encode the word vector and sentence vector respectively and the final representation of the text is obtained by the weighted sum of attention mechanism.On this basis,the auxiliary network is designed to evaluate the sentiment of words and sentences.The evaluation score is used to adjust the distribution of attention weight.After exploring the influence of sentiment information of text on classification performance,on the basis of hierarchical representation,the model is further prompted to focus on the information with strong sentiment color through the auxiliary network.Experimental results on four commonly used sentiment classification datasets show that the proposed framework can focus on the sentiment expression in the text and obtain high classification accuracy.