首页> 外文会议>Multidisciplinary information retrieval >A Machine Learning Approach for Subjectivity Classification Based on Positional and Discourse Features

【24h】

A Machine Learning Approach for Subjectivity Classification Based on Positional and Discourse Features

机译：基于位置和话语特征的机器学习主观分类方法

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

In recent years, several machine learning methods have been proposed to detect subjective (opinionated) expressions within on-line documents. This task is important in many Opinion Mining and Sentiment Analysis applications. However, the opinion extraction process is often done with rough content-based features. In this paper, we study the role of structural features to guide sentence-level subjectivity classification. More specifically, we combine classical n-grams features with novel features defined from positional information and from the discourse structure of the sentences. Our experiments show that these new features are beneficial in the classification of subjective sentences.

机译：近年来，已经提出了几种机器学习方法来检测在线文档中的主观（有主意的）表达。在许多意见挖掘和情感分析应用程序中，此任务很重要。但是，意见提取过程通常使用基于内容的粗略功能来完成。在本文中，我们研究了结构特征在指导句子级主观性分类中的作用。更具体地说，我们将经典的n-gram特征与从位置信息和句子的语篇结构定义的新颖特征相结合。我们的实验表明，这些新功能有助于主观句子的分类。

著录项

来源
《Multidisciplinary information retrieval》|2013年|17-28|共12页
会议地点 Limassol(CY)
作者
Jose M. Chenlo; David E. Losada;
展开▼
作者单位

Centro de Investigacion en Tecnoloxias da Informacion (CITIUS) Universidad de Santiago de Compostela, Spain;

Centro de Investigacion en Tecnoloxias da Informacion (CITIUS) Universidad de Santiago de Compostela, Spain;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Information Retrieval; Opinion Mining; Subjectivity Classification; Sentiment Analysis; Machine Learning; Rhetorical Structure Theory;

机译：信息检索；意见挖掘；主观性分类；情绪分析；机器学习；修辞结构理论;

相似文献

外文文献
中文文献
专利

1. Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning [J] . Rostami Omid, Kaveh Mehrdad Computational Geosciences . 2021,第3期

机译：基于生物地理的优化（BBO），人造蜜蜂（ABC）和支持向量机（SVM）的SAR图像分类的最佳特征选择：优化和机器学习的组合方法
2. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal [J] . Ebrahimzadeh Elias, Kalantari Maede, Joulani Mohammadamin, Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2018,第期

机译：阵发性心房颤动的预测：基于机器学习的方法，使用组合特征向量和专家分类对HRV信号的混合
3. Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods [J] . María del Pilar Salas-Zarate, Mario Andres Paredes-Valverde, Jorge Limon-Romero, Journal of Universal Computer Science . 2016,第5期

机译：西班牙语评论的情感分类：基于特征选择和机器学习方法的方法
4. A Machine Learning Approach for Subjectivity Classification Based on Positional and Discourse Features [C] . Jose M. Chenlo, David E. Losada Information Retrieval Facility Conference . 2013

机译：基于位置和话语特征的主观性分类机器学习方法
5. Personalized Head-Related Transfer Function from Anthropometric Features Using Two Machine Learning Models: Approach and Subjective Validation [D] . Lee, MingYang. 2020

机译：使用两台机器学习模型的人体测量特征的个性化头相关传递函数：方法和主观验证
6. Identification of donor splice sites using support vector machine: a computational approach based on positional compositional and dependency features [O] . Prabina Kumar Meher, Tanmaya Kumar Sahu, A. R. Rao, 2016

机译：使用支持向量机识别供体剪接位点：一种基于位置组成和依赖性特征的计算方法
7. The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach [O] . Samreen Naeem, Aqib Ali, Christophe Chesneau, 2021

机译：基于多光谱和纹理特征的药用植物叶片使用机器学习方法分类

A Machine Learning Approach for Subjectivity Classification Based on Positional and Discourse Features

摘要

著录项

相似文献

相关主题

期刊订阅