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Classification – Detecting
Poor Answers
Now that we are able to extract useful features from text, we can take on the
challenge of building a classifier using real data. Let's go back to our imaginary
website in Chapter 3, Clustering – Finding Related Posts, where users can submit
questions and get them answered.
A continuous challenge for owners of these Q&A sites is to maintain a decent
level of quality in the posted content. Websites such as stackoverflow.com take
considerable efforts to encourage users to score questions and answers with badges
and bonus points. Higher quality content is the result, as users are trying to spend
more energy on carving out the question or crafting a possible answer.
One particular successful incentive is the possibility for the asker to flag one
answer to their question as the accepted answer (again, there are incentives for
the asker to flag such answers). This will result in more score points for the author
of the flagged answer.
Would it not be very useful for the user to immediately see how good their answer is
while they are typing it in? This means that the website would continuously evaluate
their work-in-progress answer and provide feedback as to whether the answer shows
signs of being a poor one or not. This will encourage the user to put more effort into
writing the answer (for example, providing a code example, including an image, and
so on). So finally, the overall system will be improved.
Let us build such a mechanism in this chapter.