基于信息推理的网络新闻在线评论情绪分类

Information Inference Based Sentiment Classification for Online News Comments

网络评论数据的情绪倾向性信息对于企业商业智能系统、政府舆情分析等诸多领域有着广阔的应用空间和发展前景。该文基于语言类比超空间(HAL空间),利用信息推理方法,给出了一种短语级别的评论数据情绪倾向分类模型。该模型首先从评论文本中抽取符合预定义模式的短语,然后运用基于HAL空间的概念组合算法,将短语组合为概念C,最后使用信息推理算法,对概念C按情绪分类。实验表明,与SVM算法和Term-Count算法相比,该文的模型对于网络在线新闻评论数据分类效果较好。

Automatic classification of online comments according to their sentiment orientation is a complicated task with promising application in many domains such as enterprise intelligence system, administration of public emergencies and so on. Based on the Hyperspace Analogue to Language （HAL space） and the information inference, we propose a new model for classification of online comments in accordance with implicit or explicit sentiment expressed in comments. We first extract phrases which match our pre-defined patterns from sentences in comments, and then based on HAl. space, a conception combination algorithm is employed to blend the words in the extracted phrases into one conception, whose sentiment orientation is calculated by the proposed information inference model. Finally, the sum of information inference degree of individual phrases indicates the sentimental polarity of comments. The experiment results show that, compared to SVM and Term Count algorithms based on valence shifters and sentimental words table, our model performs better and achieves an accuracy as high as 89 %.