论文标题
基于概率图形模型和复发性神经网络的语义情感分析
Semantic Sentiment Analysis Based on Probabilistic Graphical Models and Recurrent Neural Network
论文作者
论文摘要
情感分析是基于文本形式表达的情感对文档进行分类的任务，可以使用词汇和语义方法来实现这一点。这项研究的目的是研究基于概率图形模型和复发性神经网络进行语义分析的使用。在经验评估中，将图形模型的分类性能与一些传统的机器学习分类器和经常性神经网络进行了比较。用于实验的数据集是IMDB电影评论，亚马逊消费者产品评论和Twitter评论数据集。经过这项实证研究，我们得出的结论是，随着语义特征提取方法减少分类中的不确定性，将语义纳入情感分析任务可以大大提高分类器的性能，从而实现了更准确的预测。
Sentiment Analysis is the task of classifying documents based on the sentiments expressed in textual form, this can be achieved by using lexical and semantic methods. The purpose of this study is to investigate the use of semantics to perform sentiment analysis based on probabilistic graphical models and recurrent neural networks. In the empirical evaluation, the classification performance of the graphical models was compared with some traditional machine learning classifiers and a recurrent neural network. The datasets used for the experiments were IMDB movie reviews, Amazon Consumer Product reviews, and Twitter Review datasets. After this empirical study, we conclude that the inclusion of semantics for sentiment analysis tasks can greatly improve the performance of a classifier, as the semantic feature extraction methods reduce uncertainties in classification resulting in more accurate predictions.
