Bi-path Evolution Model for Online Topic Model Based on LDA
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摘要: 网络舆情分析中需要处理大量时效性较强的文本数据流. 针对在线时效性较强的文本数据流, 提出基于LDA (Latent Dirichlet allocation)的双通道在线主题演化模型(Bi-path evolution online-LDA, BPE-OLDA), 在下一时间片生成文本时考虑文本的内容遗传和强度遗传, 很好地模拟了人在生成时效性较强的文本时的特征. 估算模型参数时对 Gibbs 采样算法进行了简化, 实验证明, 使用简化后的在线 Gibbs 重采样算法, BPE-OLDA 模型在提取时效性较强的文本数据流的主题方面具有明显的效果.Abstract: There are a large number of time-sensitive texts as data streams to be processed in open-source intelligence analysis. We design a new bi-path evolution model based online-LDA (BPE-OLDA) for the time-limited text streams. This model takes consideration of both content and intensity influences to model the composition process of human successfully. When estimating the parameters of this model, we simplify the Gibbs sampling. Experiments show that BPE-OLDA performs better than other approaches over time-limited text streams.
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Key words:
- Time-sensitive /
- intensity influence /
- Gibbs sampling /
- latent Dirichlet allocation (LDA)
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