Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet
This work addresses the challenge of deriving accurate prior polarities for sentiment analysis, which is incremental as it builds on and improves existing SentiWordNet-based methods.
The paper tackled the problem of estimating prior polarity scores for words in sentiment analysis by comparing and blending existing and new techniques within a learning framework, resulting in a new state-of-the-art approach that consistently outperformed single metrics across tasks and datasets.
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.