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Sem Eval2014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.

We present a method for emotion classification based on word vector learning which considering the inner patterns and emotion labels of words. Based on the CBOW model, we introduce the inner patterns and the emotion label, in order to enrich the emotional semantics of the word vectors. For one input document, according to the TF-IDF weight of the word, we use the weighted linear combination as the text representation. We use the word vectors or text vectors as the input of the emotion classifier, using machine learning classification method (LR, SVM, CNN), to verify the experimental results in emotion classification task. Experiments show that the presented algorithm performs better than CBOW model.

We present a method for sentiment classification based on sentiment-specific word embedding (SSWE).Word embedding is the distributed vector representation of a word with fixed length in real topological space.Algorithms for learning word embedding,like word2vec,obtain this representation from large un-annotated corpus,without considering sentiment information.We make sentiment improvement for the initial word embedding and get the sentiment-specific word embedding that contains both syntactic and sentiment information.Then text representations are built based on sentiment-specific word embeddings.Sentiment polarities of texts are obtained through machine learning approaches.Experiments show that the presented algorithm performs better than sentiment classification method based on texts modeling by word,N-gram and word embeddings from word2vec.

Recently, Deep Convolutional Neural Networks (CNNs) have been widely applied to sentiment analysis of short texts. Naturally, word embedding techniques are used to learn continuous word representations for constructing sentence matrix as input to CNN. As for sentiment analysis of customer reviews, we argue that it is problematic to learn a single representation for a word while ignoring sentiment information and the discussed aspects. In this poster, we propose a novel word embedding model to learn sentimental word embedding given specific aspects by modeling both sentiment and syntactic context under the specific aspects. We apply our method as input to CNN for sentiment analysis in multiple domains. Experiments show that the CNN based on the proposed model can consistently achieve superior performance compared to CNN based on traditional word embedding method.

网络是目前最重要的信息传播渠道,其自由性和丰富性使得信息迅速传播。挖掘网络中的热点主题对政府政策的制定、企业经营决策的调整可以提供强有力的支持,并能够满足网民对热点主题的关注需求。主题数量的庞大使得主题热度值的计算尤为重要,该文分析热度的形成原因,基于因果模型并采用面板数据,给出一种较为客观可行的主题热度计算模型。该模型使用易于获取的数据进行计算,给出较为客观的热度度量,进而便于不同主题、不同日期间的热度对比。在此基础上,通过对热度变化规律的考察,提出一种基于多峰高斯曲线拟合热度变化进行主题热度预测的思路。