微博情感倾向性分析旨在发现用户对热点事件的观点态度。由于微博噪声大、新词多、缩写频繁、有自己的固定搭配、上下文信息有限等原因,微博情感倾向性分析是一项有挑战性的工作。该文主要探讨利用卷积神经网络进行微博情感倾向性分析的可行性,分别将字级别词向量和词级别词向量作为原始特征,采用卷积神经网络来发现任务中的特征,在COAE2014任务4的语料上进行了实验。实验结果表明,利用字级别词向量及词级别词向量的卷积神经网络分别取得了95.42%的准确率和94.65%的准确率。由此可见对于中文微博语料而言,利用卷积神经网络进行微博情感倾向性分析是有效的,且使用字级别的词向量作为原始特征会好于使用词级别的词向量作为原始特征。

Chinese micro-blog sentiment analysis aims to discover the user attitude towards hot events. This task is challenged by immense noises, rich new words, numerous abbreviations, vigorous collocation, together with the limited contextual information provided in the short texts. This paper explores the feasibility of performing Chinese micro-blog sentiment analysis by convolutional neural networks. To avoid task-specific features, character level embedding and word level embedding are adopted for convolutional neural networks(CNN). On the COAE 4th task corpus, the character level CNN achieves a sentiment prediction (in both binary positive/negative classification) accuracy of 95.42%, slightly better than the word level CNN yielding 94.65% accuracy. The results show that the convolutional neural networks model is promising in Chinese micro-blog sentiment analysis.
Key words deep learning;sentiment analysis;convolutional neural networks;word embedding