How to normalize/standardize when the number of observations is different? I want to measure sentiment for reviews. I have the sentiment measure for each single sentence in the review, but the length of the reviews are different. I cannot simply add up the scores because longer reviews tend to get higher scores, and taking average is suboptimal, since the scores will have average tendency. Imagine the following example:
review 1 has one sentence:
r1 = ["I like it."] which is moderately positive: r1 = [.7]

review 2 has two sentences:
r2: ["I absolutely love it. I bought it for my dad. He uses it every day"]
the first sentence is very positive but the rest are neutral r2 = [1, .5, .5]

A human rater likely rates r2 higher than r1. adding up the scores is not fair(!) and averaging decreases the accuracy of measurement.
Any suggestions?

Train the model using whole reviews, not just sentence by sentence. That seems to be your goal: the sentiment of the review as a whole.
If you must use sentences, I think averaging is fine. Otherwise you could define your own function so long as you justify it, and the prediction is valid in your classification scheme (between 0 and 1 by the sounds).
Adding up will lead to an invalid classification if you are classifying between 0 and 1 (e.g. 1 + 2*0.5 = 2... 200% positive sentiment?).
