I have 1 short string of text (let's say it's a tweet, max 140 characters):
"A review of my beloved Roku 3 media player"
I also have a larger body of text (like a blog article, hundreds of words) which I know is related to the tweet:
"The Roku 3 media player is a great way to watch your favorite ...."
The tweet and blog article are both about the Roku 3 media player specifically. Same author, and they share many of the same phrases, words, collocations, etc. It's likely that the string "Roku 3" appears in the text, along with variations like ""Roku 3 streaming media player", "Roku 3 player" etc
I then have 10 other tweets, some which are related to the "Roku 3 media player", some of which are not (but very similar):
RELATED "A good review of the Roku 3 media player"
UNRELATED "The Roku 2 media player review"
RELATED "Roku 3 is amazing"
RELATED "The Roku 3 is better than the Roku 2 by far"
RELATED "The Roku version 3 streaming media player, fully reviewed"
UNRELATED "A comparison review of the top 3 media player boxes. Roku, Android, Toshiba"
RELATED "Roku 3 streaming media player reviewed"
Those are some examples, and I would have 10 in total. All of the tweets contain "Roku", some are about "Roku 2" and are unrelated, one uses "Roku version 3" and is related etc. Obviously, this is a very small data set.
What is the best method to classify each of the 10 tweets as relevant or not, in relation to the first tweet and blog article? What sort of features would be useful?