Correlation-Based Method for Sentiment Classification
This work addresses the need for more interpretable and simpler sentiment classification methods, though it appears incremental as it builds on existing correlation concepts.
The authors tackled sentiment classification of short texts by proposing a new classifier based on correlation metrics to measure word-label associations, which outperformed classic supervised algorithms in evaluations.
The classic supervised classification algorithms are efficient, but time-consuming, complicated and not interpretable, which makes it difficult to analyze their results that limits the possibility to improve them based on real observations. In this paper, we propose a new and a simple classifier to predict a sentiment label of a short text. This model keeps the capacity of human interpret-ability and can be extended to integrate NLP techniques in a more interpretable way. Our model is based on a correlation metric which measures the degree of association between a sentiment label and a word. Ten correlation metrics are proposed and evaluated intrinsically. And then a classifier based on each metric is proposed, evaluated and compared to the classic classification algorithms which have proved their performance in many studies. Our model outperforms these algorithms with several correlation metrics.