Enhancing Sentiment Analysis Results through Outlier Detection Optimization

TL;DR

This paper explores the use of outlier detection with Deep SVDD to improve sentiment analysis accuracy by removing inconsistent labels, demonstrating benefits across various models and datasets.

Contribution

It introduces a method combining outlier detection with Deep SVDD to enhance sentiment classification results in subjective text datasets.

Findings

Outlier removal improves classification accuracy in most datasets.

Large language models like DeBERTa capture complex patterns effectively.

Outlier detection benefits are consistent across different classifiers.

Abstract

When dealing with text data containing subjective labels like speaker emotions, inaccuracies or discrepancies among labelers are not uncommon. Such discrepancies can significantly affect the performance of machine learning algorithms. This study investigates the potential of identifying and addressing outliers in text data with subjective labels, aiming to enhance classification outcomes. We utilized the Deep SVDD algorithm, a one-class classification method, to detect outliers in nine text-based emotion and sentiment analysis datasets. By employing both a small-sized language model (DistilBERT base model with 66 million parameters) and non-deep learning machine learning algorithms (decision tree, KNN, Logistic Regression, and LDA) as the classifier, our findings suggest that the removal of outliers can lead to enhanced results in most cases. Additionally, as outliers in such datasets are not necessarily unlearnable, we experienced utilizing a large language model -- DeBERTa v3 large with 131 million parameters, which can capture very complex patterns in data. We continued to observe performance enhancements across multiple datasets.