I'm trying to extract text from arbitrary html pages. Some of the pages (which I have no control over) have malformed html or scripts which make this difficult. Also I'm on a shared hosting environment, so I can install any python lib, but I can't just install anything I want on the server.
pyparsing and html2text.py also did not seem to work for malformed html pages.
Example URL is http://apnews.myway.com/article/20091015/D9BB7CGG1.html
My current implementation is approximately the following:
# Try using BeautifulSoup 3.0.7a
soup = BeautifulSoup.BeautifulSoup(s)
comments = soup.findAll(text=lambda text:isinstance(text,Comment))
[comment.extract() for comment in comments]
c=soup.findAll('script')
for i in c:
i.extract()
body = bsoup.body(text=True)
text = ''.join(body)
# if BeautifulSoup can't handle it,
# alter html by trying to find 1st instance of "<body" and replace everything prior to that, with "<html><head></head>"
# try beautifulsoup again with new html
if beautifulsoup still does not work, then I resort to using a heuristic of looking at the 1st char, last char (to see if they looks like its a code line # < ; and taking a sample of the line and then check if the tokens are english words, or numbers. If to few of the tokens are words or numbers, then I guess that the line is code.
I could use machine learning to inspect each line, but that seems a little expensive and I would probably have to train it (since I don't know that much about unsupervised learning machines), and of course write it as well.
Any advice, tools, strategies would be most welcome. Also I realize that the latter part of that is rather messy since if I get a line that is determine to contain code, I currently throw away the entire line, even if there is some small amount of actual English text in the line.