Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet

TL;DR

This paper compares various methods for deriving prior polarities from SentiWordNet and introduces a learning framework that improves accuracy, revealing biases related to Part of Speech and annotator gender.

Contribution

It presents a novel learning-based approach that combines multiple techniques for better prior polarity estimation from SentiWordNet.

Findings

Learning approach outperforms individual metrics

State-of-the-art results achieved in prior polarity estimation

Biases identified related to Part of Speech and annotator gender

Abstract

Assigning a positive or negative score to a word out of context (i.e. a word's prior polarity) 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 techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification 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.