This paper proposes two new algorithms for classifying objects with categorical attributes. These algorithms are derived from the assumption that the attributes of different object classes have different probability d...This paper proposes two new algorithms for classifying objects with categorical attributes. These algorithms are derived from the assumption that the attributes of different object classes have different probability distributions. One algorithm classifies objects based on the distribution of the attribute frequencies, and the other classifies objects based on the distribution of the pairwise attribute frequencies described using a matrix of pairwise frequencies. Both algorithms are based on the method of invariants, which offers the simplest dependencies for estimating the probabilities of objects in each class by an average frequency of their attributes. The estimated object class corresponds to the maximum probability. This method reflects the sensory process models of animals and is aimed at recognizing an object class by searching for a prototype in information accumulated in the brain. Because these matrices may be sparse, the solution cannot be determined for some objects. For these objects, an analog of the k-nearest neighbors method is provided in which for each attribute value, the class to which the majority of the k-nearest objects in the training sample belong is determined, and the most likely class value is calculated. The efficiencies of these two algorithms were confirmed on five databases.展开更多
情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等...情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等因素对分类性能的影响更是亟待研究的问题。本文以n-gram以及名词、动词、形容词、副词作为不同的文本表示特征,以互信息、信息增益、CHI统计量和文档频率作为不同的特征选择方法,以中心向量法、KNN、Winnow、Na ve Bayes和SVM作为不同的文本分类方法,在不同的特征数量和不同规模的训练集情况下,分别进行了中文情感分类实验,并对实验结果进行了比较,对比结果表明:采用Bi Grams特征表示方法、信息增益特征选择方法和SVM分类方法,在足够大训练集和选择适当数量特征的情况下,情感分类能取得较好的效果。展开更多
产品评论的情感倾向性分析是一个很有研究价值的领域,可以帮助客户、商家进行决策。针对产品评论中的属性词和情感词在文本中的各种关系,制定了8组特征选择规则,利用SVM算法训练模型来判断属性词和情感词的搭配识别,进而依据情感词及否...产品评论的情感倾向性分析是一个很有研究价值的领域,可以帮助客户、商家进行决策。针对产品评论中的属性词和情感词在文本中的各种关系,制定了8组特征选择规则,利用SVM算法训练模型来判断属性词和情感词的搭配识别,进而依据情感词及否定词等分析属性特征的情感倾向。实验结果表明:提出的基于S V M的搭配识别方法,在识别属性特征与情感词的搭配方面具有不错的分类效果。展开更多
文摘This paper proposes two new algorithms for classifying objects with categorical attributes. These algorithms are derived from the assumption that the attributes of different object classes have different probability distributions. One algorithm classifies objects based on the distribution of the attribute frequencies, and the other classifies objects based on the distribution of the pairwise attribute frequencies described using a matrix of pairwise frequencies. Both algorithms are based on the method of invariants, which offers the simplest dependencies for estimating the probabilities of objects in each class by an average frequency of their attributes. The estimated object class corresponds to the maximum probability. This method reflects the sensory process models of animals and is aimed at recognizing an object class by searching for a prototype in information accumulated in the brain. Because these matrices may be sparse, the solution cannot be determined for some objects. For these objects, an analog of the k-nearest neighbors method is provided in which for each attribute value, the class to which the majority of the k-nearest objects in the training sample belong is determined, and the most likely class value is calculated. The efficiencies of these two algorithms were confirmed on five databases.
文摘情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等因素对分类性能的影响更是亟待研究的问题。本文以n-gram以及名词、动词、形容词、副词作为不同的文本表示特征,以互信息、信息增益、CHI统计量和文档频率作为不同的特征选择方法,以中心向量法、KNN、Winnow、Na ve Bayes和SVM作为不同的文本分类方法,在不同的特征数量和不同规模的训练集情况下,分别进行了中文情感分类实验,并对实验结果进行了比较,对比结果表明:采用Bi Grams特征表示方法、信息增益特征选择方法和SVM分类方法,在足够大训练集和选择适当数量特征的情况下,情感分类能取得较好的效果。
文摘产品评论的情感倾向性分析是一个很有研究价值的领域,可以帮助客户、商家进行决策。针对产品评论中的属性词和情感词在文本中的各种关系,制定了8组特征选择规则,利用SVM算法训练模型来判断属性词和情感词的搭配识别,进而依据情感词及否定词等分析属性特征的情感倾向。实验结果表明:提出的基于S V M的搭配识别方法,在识别属性特征与情感词的搭配方面具有不错的分类效果。
