关键词: 情感分析, 弱监督, 预训练-微调, 卷积神经网络

Abstract: Traditional researches of sentiment analysis are mostly based on machine learning algorithm, which rely on a huge number of artificially extracted features and domain knowledge. Convolution neural network is used to automatically learn the characteristics of texts and then identify the sentiment polarity of them. In order to solve the problem of insufficient supervision training dataset in sentiment analysis, the large-scale distant supervision data the used to train convolution neural network. At the same time, the “pre-train-fine-tune” strategy is used to overcome the noises in the distant supervision data, by pre-training convolution neural network on the distant supervision data and then fine-tuning it on the supervision dataset. Experimental results on the SemEval-2013 Twitter sentiment analysis dataset show that the ability of convolutional neural network to learn emotion semantics is enhanced effectively by using distant supervision data to participate in the training.

Key words: sentiment analysis, distant supervision, pre-train-fine-tune, Convolutional Neural Networks（CNN）