Presented in this paper is a language modeling approach to the sentiment classification of text.
提出了一种基于语言建模的文本情感分类的方法。
This paper presents the weighted linear combination method for the sentence sentiment classification based on Chinese sentiment word table.
提出了一种基于汉语情感词词表的加权线性组合的句子情感分类方法。
Next, in the words, sentences tend to study the basis of sentiment, sentiment classification for Chinese text specific system design and application.
其次，在词汇、句子情感倾向研究的基础之上，对中文文本情感倾向进行了具体的分析计算及设计应用。
This paper introduces a text filtering system merging topic classification based on vector space model and sentiment classification based on support vector machine.
该文介绍了一种文本过滤算法，该算法把基于空间向量模型的主题分类算法与基于支持向量机文本态度分类结合起来。
This paper proposes a method based on granularity computing to get the sentiment classification of text comments through rule learning by establishing the granule network.
提出了一种基于粒运算的方法，通过建立粒网络生成分类规则，从而得到评论文本的情感倾向分类。
The experiment results indicate that the greater text sentiment classification impact depends on other corpus, excluded adjective, verb, adverb as stop words and none stop words.
本文利用三种特征选择方法、两种权重计算方法、五种停用词表以及支持向量机分类器对汽车语料的文本情感类别进行了研究。
Text classification of objects related to sentiment words, sentences, paragraphs and chapters.
文本中涉及到情感分类的对象一般有词汇、句子、段落以及篇章。
Text classification of objects related to sentiment words, sentences, paragraphs and chapters.
文本中涉及到情感分类的对象一般有词汇、句子、段落以及篇章。
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