基于词性标注序列特征提取的微博情感分类

传统的n-gram文本特征提取方法会产生高维度的特征向量，高维数据不但增大了分类的难度，同时也会增加分类的时间。针对这一问题，提出了一种基于词性（POS）标注序列的特征提取方法，根据词性序列能够代表一类文本的这一个特点，利用词性序列组作为文本的特征以达到降低特征维度的效果。在实验中，词性序列特征提取方法比n-gram特征提取方法至少提高了9%的分类精度，降低4816个维度。实验结果表明，该方法能够适用于微博情感分类。

Emotion classification with feature extraction based on part of speech tagging sequences in micro blog

Traditional n-gram feature extraction tends to produce a high-dimensional feature vector. High-dimensional data not only increases the difficulty of classification, but also increases the classification time. Aiming at this problem, this paper presented a feature extraction method based on Part-of-Speech (POS) tagging sequences. The principle of this method was to use POS sequences as text features to reduce feature dimension, according to the property that POS sequences can represent a kind of text.In the experiment,compared with the n-gram feature extraction, the feature extraction based on POS sequences at least improved the classification accuracy of 9% and reduced the dimension of 4816. The experimental results show that the method is suitable for emotion classification in micro blog.