基于不平衡情感分类的Lasso-Lars特征选择方法研究

万会芳,闵兰,舒畅

doi:10.13718/j.cnki.xsxb.2018.09.012

西南师范大学学报(自然科学版)

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基于不平衡情感分类的Lasso-Lars特征选择方法研究

万会芳,闵兰,舒畅. 基于不平衡情感分类的Lasso-Lars特征选择方法研究[J]. 西南师范大学学报(自然科学版), 2018, 43(9): 74-78. doi: 10.13718/j.cnki.xsxb.2018.09.012

引用本文:

万会芳,闵兰,舒畅. 基于不平衡情感分类的Lasso-Lars特征选择方法研究[J]. 西南师范大学学报(自然科学版), 2018, 43(9): 74-78. doi: 10.13718/j.cnki.xsxb.2018.09.012

WAN Hui-fang, MIN Lan, SHU Chang. Feature Selection in Imbalanced Sentiment Classification: A Method Using Lasso-Lars[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(9): 74-78. doi: 10.13718/j.cnki.xsxb.2018.09.012

Citation:

WAN Hui-fang, MIN Lan, SHU Chang. Feature Selection in Imbalanced Sentiment Classification: A Method Using Lasso-Lars[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(9): 74-78. doi: 10.13718/j.cnki.xsxb.2018.09.012

基于不平衡情感分类的Lasso-Lars特征选择方法研究

万会芳,闵兰,舒畅

成都理工大学管理科学学院, 成都 610059

Feature Selection in Imbalanced Sentiment Classification: A Method Using Lasso-Lars

WAN Hui-fang, MIN Lan, SHU Chang

College of Management Science, Chengdu University of Technology, Chengdu 610059, China

摘要: 基于Lasso回归和支持向量机分类器，首先利用Lasso回归具有变量筛选的特点，过滤部分不重要的特征，然后利用支持向量机分类器做情感提取.在某化妆品品牌的评论数据实验中，利用基础情感词典和领域情感词典构建待选择高维特征集，通过对比特征选择前后的G-means，精确度和召回率等，均取得显著效果.
- 不平衡情感分类 /
- 特征选择 /
- Lasso
Abstract: The characteristics of textual emotion analysis are usually of high dimension and sparseness. Lasso has a simple and efficient trait in feature selection. This paper introduces the Lasso regression into the unbalanced emotion analysis and achieves remarkable results. Applying emotional analysis in e-commerce plays an important role in improving product quality and improving service, which attracts many researchers and has high research value. In fact, the number of positive comments on e-commerce data generally exceeds the number of bad reviews. If the feature selection is not reasonable, it is easy to ignore the bad reviews, and the bad reviews are the key to analyzing the problems. Based on the Lasso regression and SVM classifier, this paper first uses Lasso regression to filter the features that have variable screening, filters some unimportant features, and then makes use of SVM classifier to extract the emotion. In a cosmetic brand's reviewing data experiment, the basic emotion dictionary and domain sentiment lexicon are used to construct the high-dimensional feature set to be selected, and the significant effects are achieved by comparing G-means before and after feature selection, accuracy and recall.
- imbalanced sentiments classification /
- feature selection /
- Lasso .

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基于不平衡情感分类的Lasso-Lars特征选择方法研究

万会芳,闵兰,舒畅

成都理工大学管理科学学院, 成都 610059

收稿日期: 2017-12-10

关键词:

摘要: 基于Lasso回归和支持向量机分类器，首先利用Lasso回归具有变量筛选的特点，过滤部分不重要的特征，然后利用支持向量机分类器做情感提取.在某化妆品品牌的评论数据实验中，利用基础情感词典和领域情感词典构建待选择高维特征集，通过对比特征选择前后的G-means，精确度和召回率等，均取得显著效果.

English Abstract

Feature Selection in Imbalanced Sentiment Classification: A Method Using Lasso-Lars

成都理工大学管理科学学院, 成都 610059

Received Date: 2017-12-10

Keywords:

Abstract: The characteristics of textual emotion analysis are usually of high dimension and sparseness. Lasso has a simple and efficient trait in feature selection. This paper introduces the Lasso regression into the unbalanced emotion analysis and achieves remarkable results. Applying emotional analysis in e-commerce plays an important role in improving product quality and improving service, which attracts many researchers and has high research value. In fact, the number of positive comments on e-commerce data generally exceeds the number of bad reviews. If the feature selection is not reasonable, it is easy to ignore the bad reviews, and the bad reviews are the key to analyzing the problems. Based on the Lasso regression and SVM classifier, this paper first uses Lasso regression to filter the features that have variable screening, filters some unimportant features, and then makes use of SVM classifier to extract the emotion. In a cosmetic brand's reviewing data experiment, the basic emotion dictionary and domain sentiment lexicon are used to construct the high-dimensional feature set to be selected, and the significant effects are achieved by comparing G-means before and after feature selection, accuracy and recall.