基于文本分类的商品评价情感分析  

Sentiment analysis for goods evaluation based on text classification

作　　者：钟将[1] 杨思源[1] 孙启干[1] 

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《计算机应用》2014年第8期2317-2321,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61103114);中央高校基本科研业务费资助项目(CDJZR 185502)

摘　　要：为了在准确判断商品评价情感倾向的同时提高识别效率,提出了基于矩阵投影(MP)和归一化向量(NLV)的文本分类算法实现对商品评价的情感分析。首先,利用矩阵投影提取商品评价的特征词;然后,计算每一类别中特征词的平均特征频率(FF),采用归一化函数(NLF)对平均特征频率进行归一化处理,得到每一类别的归一化向量;最后,通过比较评价的特征向量与每一类别的归一化向量的相似度预测评价的情感倾向。与k近邻(kNN)、朴素贝叶斯(NB)和支持向量机(SVM)算法进行了对比,实验结果表明该算法具有较高的预测准确度和分类速度:尤其与kNN算法相比该算法有明显优势,该算法的宏平均F1值比kNN高出12%以上,分类时间缩短了11/12;与SVM算法相比分类速度也大幅提高。To improve the efficiency of recognition while determining the emotional tendencies of goods evaluation accurately, this paper proposed a text classification approach based on Matrix Projection （MP） and Normalized Vector （NLV） to realize sentiment analysis for goods evaluation. Firstly, this approach extracted feature words of goods evaluation by utilizing matrix projection, and then computed the average Feature Frequency （FF） of feature words in each category, and obtained normalized vector through normalized processing to feature frequency of each category by using Normalized Function （NLF）. Finally, it predicted the sentiment tendency by comparing similarity between feature vector of goods evaluation and normalized vector of each category. Compared with the k-Nearest Neighbor （kNN）, Naive Bayesian （NB） and Support Vector Machine （SVM） algorithm, the experimental results show that the proposed approach has higher prediction accuracy and speed of classification. Especially compared with the kNN the approach has obvious advantages, its macro average F1 value is more than 12% higher than the kNN and classification time is reduced by 11/12〖BP（〗reduce to或reduce by〖BP）〗. Compared with the SVM its speed is greatly improved.

关 键 词：商品评价 情感分析 文本分类 矩阵投影 归一化向量 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]