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Updated: Jun 12, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
在空间时间数据上的Top-k情绪分析.
Abdulaziz Almaslukh1, Aisha Almaalwy1, Nasser Allheeib1
1Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
这项研究引入了分析社交媒体情绪的有效框架,加快了在X (以前的Twitter) 等平台上对基于位置的,时间敏感的公众意见的搜索. 新的查询方法提高了搜索时间的十倍.
科学领域:
- 社交媒体分析 社交媒体分析
- 自然语言处理自然语言处理.
- 数据挖掘 数据挖掘
背景情况:
- 像X (以前的Twitter) 这样的社交媒体平台产生了大量适合情绪分析的数据.
- 现有的情绪分析往往包括空间和时间维度,以提高准确性.
- 需要更快的,特定于位置的,对最近社交媒体帖子的情绪分析.
研究的目的:
- 开发数据索引和搜索查询的一般框架,以简化和加快社交媒体数据的情绪分析.
- 通过情绪分类来增强时空数据分析.
- 根据位置和情绪,能够有效地检索最近的top-k推文.
主要方法:
- 提出了一个新的搜索查询,扩展了基本的空间距离查询.
- 综合情绪分析,将时间数据分类为积极,消极或中立.
- 在索引数据集上运行查询,以实现高效处理.
主要成果:
- 与基线方法相比,拟议的查询显示了查询时间的十倍以上的改善.
- 性能通过各种参数进行评估,包括top-k,查询距离和关键字数量.
- 该框架有效地简化了基于位置的情绪分析的搜索过程.
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结论:
- 开发的框架显著提高了搜索和分析社交媒体空间时间数据中的情绪的效率.
- 这种方法通过更快地获得相关的,本地化的情绪信息来促进对公众论的更好理解.
- 查询扩展为大型社交媒体数据集的实时情绪分析提供了一个可扩展的解决方案.