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You could break the text up into sentences [1] and do sentiment analysis [2] on the sentences with 'remote' in. Then flag based on that. [1] https://opennlp.ap
by louthy 12y ago
You could break the text up into sentences [1] and do sentiment analysis [2] on the sentences with 'remote' in. Then flag based on that.
[1] https://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html#tools.sentdetect https://opennlp.apache.org/documentation/1.5.3/manual/opennl...
[2] http://nlp.stanford.edu/sentiment/ http://nlp.stanford.edu/sentiment/
- davidw 12y agoOr you could hire people to do it via oDesk or Mechanical Turk. Not so interesting technically, but it's a job people are good at.
- fudged71 12y agoHire people for cheap to help people be hired for $$$, with no reward for the upsell. Brilliant! :)
- WalterGR 12y agoSentence segmentation and sentiment analysis may be overkill. N-grams + Naive Bayes is potentially Good Enough.
- bbcbasic 12y agoWikify it. Let users can log in and change the remote/non-remote status (and other attributes). Have some kind of trust system (could be linked to HN points or whatever). (Even better if the YC guys made a custom job board where you fill in a form with all the details so there is no inconsistency.)
- nl 12y agoSentiment analysis probably isn't the right option here, though it may work. I think a combination of dependency parsing[1] and regex is the way to go. regex examples: "Remote: No", "No remote please" Dependency parsing examples: ""Remote work isn’t an option", "Remote work will not be considered" [1] look for negation in the parse tree using something like http://demo.ark.cs.cmu.edu/parse?sentence=Remote%20work%20isn%25u2019t%20an%20option http://demo.ark.cs.cmu.edu/parse?sentence=Remote%20work%20is...