• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Intelligent Information Systems >Dream sentiment analysis using second order soft co-occurrences (SOSCO) and time course representations
【24h】

Dream sentiment analysis using second order soft co-occurrences (SOSCO) and time course representations

机译：使用二阶软同现（SOSCO）和时程表示法进行梦境情感分析

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

We describe a project undertaken by an interdisciplinary team combining researchers in sleep psychology and in Natural Language Processing/Machine Learning. The goal is sentiment analysis on a corpus containing short textual descriptions of dreams. Dreams are categorized in a four-level scale of positive and negative sentiments. We chose a four scale annotation to reflect the sentiment strength and simplicity at the same time. The approach is based on a novel representation, taking into account the leading themes of the dream and the sequential unfolding of associated sentiments during the dream. The dream representation is based on three combined parts, two of which are automatically produced from the description of the dream. The first part consists of co-occurrence vector representation of dreams in order to detect sentiment levels in the dream texts. Those vectors unlike the standard Bag-of-words model capture non-local relationships between meanings of word in a corpus. The second part introduces the dynamic representation that captures the sentimental changes throughout the progress of the dream. The third part is the self-reported assessment of the dream by the dreamer according to eight given attributes (self-assessment is different in many respects from the dream's sentiment classification). The three representations are subject to aggressive feature selection. Using an ensemble of classifiers on the combined 3-partite representation, the agreement between machine rating and the human judge scores on the four scales was 64 % which is in the range of human experts' consensus in that domain. The accuracy of the system was 14 % more than previous results on the same task.
机译：我们描述了一个由跨学科团队进行的项目，该团队结合了睡眠心理学和自然语言处理/机器学习领域的研究人员。目的是对包含梦想的简短文字描述的语料库进行情感分析。梦分为正面和负面情绪四个等级。我们选择了四级注释，以同时反映情感强度和简单性。该方法基于新颖的表示形式，同时考虑了梦的主要主题以及梦中相关情感的顺序展开。梦的表示基于三个组合部分，其中两个是根据梦的描述自动生成的。第一部分包括梦的共现向量表示，以检测梦文本中的情感水平。这些向量与标准的词袋模型不同，捕获了语料库中词义之间的非局部关系。第二部分介绍了动态表示，该动态表示捕获了梦的整个过程中的感性变化。第三部分是梦者根据八个给定的属性对梦进行自我报告的评估（自我评估与梦的情感分类在许多方面有所不同）。这三种表示形式都必须经过积极的功能选择。在组合的3部分表示法上使用分类器的合奏，机器等级与四个等级的人类判断得分之间的一致性为64％，这在该领域的人类专家共识中。该系统的准确性比以前在同一任务上的结果高出14％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号