What exactly is an n Gram?
sentiment-analysis
Solution
N-grams are simply all combinations of adjacent words or letters of length n that you can find in your source text. For example, given the word `fox`, all 2-grams (or “bigrams”) are `fo` and `ox`. You may also count the word boundary – that would expand the list of 2-grams to `#f`, `fo`, `ox`, and `x#`, where `#` denotes a word boundary.
You can do the same on the word level. As an example, the `hello, world!` text contains the following word-level bigrams: `# hello`, `hello world`, `world #`.
The basic point of n-grams is that they capture the language structure from the statistical point of view, like what letter or word is likely to follow the given one. The longer the n-gram (the higher the n), the more context you have to work with. Optimum length really depends on the application – if your n-grams are too short, you may fail to capture important differences. On the other hand, if they are too long, you may fail to capture the “general knowledge” and only stick to particular cases.
Problem
I found this previous question on SO: N-grams: Explanation + 2 applications. The OP gave this example and asked if it was correct: ``` Sentence: "I live in NY." word level bigrams (2 for n): "# I', "I live", "live in", "in NY", 'NY #' character level bigrams (2 for n): "#I", "I#", "#l", "li", "iv", "ve", "e#", "#i", "in", "n#", "#N", "NY", "Y#" When you have this array of n-gram-parts, you drop the duplicate ones and add a counter for each part giving the frequency: word level bigrams: [1, 1, 1, 1, 1] character level bigrams: [2, 1, 1, ...] ``` Someone in the answer section confirmed this was correct, but unfortunately I'm a bit lost beyond that as I didn't fully understand everything else that was said! I'm using LingPipe and following a tutorial which stated I should choose a value between 7 and 12 - but without stating why. What is a good nGram value and how should I take it into account when using a tool like LingPipe? Edit: This was the tutorial: http://cavajohn.blogspot.co.uk/2013/05/how-to-sentiment-analysis-of-tweets.html