Correlation-Based Method for Sentiment Classification

TL;DR

This paper introduces a simple, interpretable sentiment classifier based on correlation metrics between words and sentiment labels, outperforming traditional algorithms in accuracy.

Contribution

It proposes a novel correlation-based sentiment classification method that enhances interpretability and extends easily with NLP techniques.

Findings

The correlation-based classifier outperforms classic algorithms.

Ten correlation metrics are evaluated and compared.

The model maintains human interpretability while achieving high accuracy.

Abstract

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.