[英]Text classification & topic modelling
For a huge set of articles, I want to get the topic models with weightage assigned to different topics & within topics, what are the weightage for different sub-topics.对于大量文章,我想获得分配给不同主题和主题内权重的主题模型,不同子主题的权重是多少。 For example, if I feed an article which falls in both Business & Technology domain, then the program's output shuold be something like this :-例如,如果我提供了一篇同时属于商业和技术领域的文章,那么程序的输出应该是这样的:-
What's the best open-source language processing programs available that can successfully do this stuff?可以成功完成这些工作的最佳开源语言处理程序是什么?
您可以使用开源NLTK Toolkit进行分类。
I would give NLTK a try, but scikit-learn, even though it has a steeper learning curve than NLTK, is probably a better bet.我会尝试 NLTK,但是 scikit-learn,尽管它的学习曲线比 NLTK 更陡峭,但可能是更好的选择。 It's much more configurable.它的可配置性要强得多。
http://scikit-learn.org/stable/documentation.html http://scikit-learn.org/stable/documentation.html
There are several programs to do a part of this task, for a starter I recommend mallet .有几个程序可以完成这项任务的一部分,对于初学者,我推荐mallet 。 Note that any topic modeling program gives you the topics in the form you want, ie,请注意,任何主题建模程序都会以您想要的形式为您提供主题,即,
( 0.438 - Marketing , 0.375 - Companies, 0.062 - Office Work)
but the labels (in this example Business ) you need to assign yourself.但是您需要自己分配标签(在本例中为Business )。 Mallet also gives you a decomposition of the text to the topics (identified by numbers, not by the labels). Mallet 还为您提供了文本到主题的分解(由数字标识,而不是由标签标识)。
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