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Many approaches to sentiment analysis rely on lexica where words are taggedwith their prior polarity - i.e. if a word out of context evokes somethingpositive or something negative. In particular, broad-coverage resources likeSentiWordNet provide polarities for (almost) every word. Since words can havemultiple senses, we address the problem of how to compute the prior polarity ofa word starting from the polarity of each sense and returning its polaritystrength as an index between -1 and 1. We compare 14 such formulae that appearin the literature, and assess which one best approximates the human judgementof prior polarities, with both regression and classification models.

Assessing Sentiment Strength in Words Prior Polarities