预测用户的位置情绪,考虑到多维时空依赖性
Wei Jiang1,2, Yiming Wang1,2, Xiaoqing Song1,2
1Anhui Normal University, Wuhu, China.
Frontiers in psychology
|October 24, 2025
概括
这项研究使用来自微博的位置数据预测城市居民的情绪. 这种新的方法在预测时空情感变化方面达到75%的准确性,优于现有的技术.
科学领域:
- 计算社会科学 计算社会科学
- 地理空间数据分析.
- 情感计算是一种情感计算.
背景情况:
- 城市居民的情绪状态表现出复杂的时空动态.
- 现有的情绪预测模型往往忽视空间维度,只关注时间序列.
- 了解和预测这些时空情绪模式对于城市福祉监测至关重要.
研究的目的:
- 提出一种新的方法,通过结合多维时空依赖来预测用户基于位置的情绪.
- 利用地理标记的社交媒体数据,更全面地了解城市情感景观.
- 通过考虑空间背景来提高情绪预测的准确性.
主要方法:
- 利用了来自上海的地理标签的微博数据.
- 应用了HiSpatialCluster算法来识别用户停留区域.
- 采用FaceReader算法从图像中检测情绪,并嵌入图形以提取停留区域的功能.
- 开发了一种基于注意力的BiLSTM模型,以捕捉时空情感依赖.
主要成果:
- 在微博数据集上实现了75%的位置情感预测准确度.
- 与单个LSTM和CNN预测方法相比,表现优越.
- 成功建模了用户情绪中的多维时空依赖性.
结论:
- 拟议的方法有效地预测了城市居民的时空情感变化.
- 这些发现加深了对城市环境中情绪变化模式的理解.
- 结果为优化基于位置的推服务和城市规划提供了潜力.
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