Standard deviation is a kind of "typical distance from the mean", usually slightly larger than the average distance from the mean. (And so it's measured in the same units as the original observations.)
So yes, as you suggest in comments, a small SD indicates that most of the distribution is close to the mean.
If the standard deviation is in the ballpark of about 0.7-1, then a typical rating is about 1 point away from the mean.
If the standard deviation is 0 they're all the same rating. (e.g. if everyone picks 1, that will have a standard deviation of 0).
Generally speaking there's no absolute standard of "large" or "small" for standard deviations (it depends on what you're doing, what the values are measuring, and on a number of other things) -- but with ratings on 1 to some maximum (like 5) there is a "biggest possible" standard deviation, which is half the range*. Since the range is 4, a standard deviation of 2 is definitely "big", representing essentially complete (and even) polarization into 1 or 5 ratings.
* (times $\sqrt{\frac{n}{n-1}}$ for $n$ observations if we're using the Bessel-corrected standard deviation)
You might also compare to the SD for a completely even spread across all 5 ratings, which would be on the "spread out" side (i.e. that would be a relatively big SD). This is a standard deviation of a bit over 1.4 ($\sqrt{2}$ -- or rather, $\sqrt{2\frac{n}{n-1}}$ with the usual Bessel correction). So with ratings on 1 - 5 you might call 1.4 "biggish".
Here's a few examples to give some basis for comparison:
