简体繁体 English

pyLDAvis 对于 lambda = 0 的条宽的含义

[英]Meaning of bar width for pyLDAvis for lambda = 0

原文 2018-06-06 17:56:28 0 2 python/ lda/ topic-modeling

Not sure if this is the right forum but I was wondering if anyone understands how to interpret the width of the red vs. blue bars on the right-hand side of pyLDAvis plots when lambda = 0 (see http://www.kennyshirley.com/LDAvis/#topic=0&lambda=0.01&term= for demo, for exsample).不确定这是否是正确的论坛，但我想知道是否有人了解当 lambda = 0 时如何解释 pyLDAvis 图右侧红色和蓝色条的宽度（请参阅http://www.kennyshirley. com/LDAvis/#topic=0&lambda=0.01&term=用于演示，用于示例）。 I understand that when lambda = 1, the red bars represent the counts of the terms in a given topic, and the gray bars represent the counts of the same terms overall in the corpus.我知道当 lambda = 1 时，红色条表示给定主题中术语的计数，灰色条表示语料库中整体相同术语的计数。 I don't understand what's displayed when lambda = 0 and why the bars don't seem to be ordered in any way anymore.我不明白当 lambda = 0 时显示什么以及为什么条形似乎不再以任何方式排序。 Could you help?你能帮忙吗？

2 个解决方案

In simple words:简单来说：

Values of lambda that are very close to zero will show terms that are more specific for a chosen topic .非常接近于零的 lambda 值将显示对所选主题更具体的术语。 Meaning that you will see terms that are "important" for that specific topic but not necessarily "important" for the whole corpus.这意味着您将看到对特定主题“重要”但不一定对整个语料库“重要”的术语。
Values of lambda that are very close to one will show those terms that have the highest ratio between frequency of the terms for that specific topic and the overall frequency of the terms from the corpus .非常接近 1 的 lambda 值将显示那些在该特定主题的术语频率与来自语料库的术语的整体频率之间具有最高比率的术语。

More info about lambda and LDAvis you can find here: LDAvis: A method for visualizing and interpreting topics有关 lambda 和 LDAvis 的更多信息，您可以在此处找到： LDAvis：一种可视化和解释主题的方法

Check out http://qpleple.com/word-relevance/查看http://qpleple.com/word-relevance/

When lambda = 1, the relevance score = P(word | topic k), in other words their frequency count.当 lambda = 1 时，相关性分数 = P(word | topic k)，换句话说，它们的频率计数。