Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet

Assigning a positive or negative score to a word out of context (i.e. a word’s prior polar- ity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used tech- niques together with newly proposed ones and incorporate all of them in a learning frame- work to see whether blending them can fur- ther improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classifi- cation models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-of- the-art approach in computing words’ prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.