I came up with an idea to use machine learning for automatic grading of topic-specific texts.
More specifically, I will first use normal text classification techniques to sort all candidate texts into topics. Then, I want to be able to judge the quality of texts in specific topics:
For instance, news articles in different topics (Technology, Sports, International, Business, etc). Firstly, every article will be sorted into one topic. And what I want to archive then is to automatically grade this 'Technology' article based on the range of 1-10 or good/poor.
The criteria for such scoring scheme may be:
- Elaboration (rich in details)
- Coverage of all key words
(It is actually a little bit hard for me to quantify my criteria. The grading criteria here can be very similar to those used when human beings instinctively and quickly judge quality of two articles on a same story.)
However, as I reckon, text classification based on 'bag of words' may not be able to perform this task well. (Or will it?)
I believe such task is well-researched, and even it is a whole field rather than text classification. But I haven't figured out how to do this by searching around. Could you please refer me to related techniques/discussions/names or give me some hints if text classification can handle that.
Many thanks in advance.