I have sentiment data for customer reviews drawn from a larger population of reviews. Each product has a number of customer reviews. Each review has a sentiment (opinion/feeling) between 0 and 1 where 0 is very negative and 1 is very positive.
Customers seldom write reviews when one is indifferent. They are either positive or negative. So the distribution of sentiments is not normal but more bimodal with some skew to the middle, so there are lots of negative reviews and lots of positive reviews but not much in the middle.
How can I measure the spread of data and compare them between different products? For example, a product can have a mean sentiment of 0.8 when it has most reviews around that value. Another product can also have a mean of 0.8 but have wildly positive reviews and some very negative. The latter product would have a larger spread of the sentiment. The products with the largest spread of sentiments is likely marketed wrongly, so it would be important to identify them: people may buy them and think they will do something it doesn't.
I assume standard deviation is out of the picture since the distribution isn't normal or t-distributed. Are there other measures of spread for this kind of bimodal distribution?