Variational Weakly Supervised Sentiment Analysis with Posterior Regularization

TL;DR

This paper introduces a variational weakly supervised sentiment analysis method using posterior regularization to improve label distribution control, resulting in more stable and accurate sentiment predictions with less reliance on human annotations.

Contribution

It proposes a novel posterior regularization framework for variational weakly supervised sentiment analysis, enhancing performance and stability over existing methods.

Findings

Improved sentiment analysis accuracy with posterior regularization.

Reduced prediction variance in sentiment classification.

Enhanced stability of weakly supervised models.

Abstract

Sentiment analysis is an important task in natural language processing (NLP). Most of existing state-of-the-art methods are under the supervised learning paradigm. However, human annotations can be scarce. Thus, we should leverage more weak supervision for sentiment analysis. In this paper, we propose a posterior regularization framework for the variational approach to the weakly supervised sentiment analysis to better control the posterior distribution of the label assignment. The intuition behind the posterior regularization is that if extracted opinion words from two documents are semantically similar, the posterior distributions of two documents should be similar. Our experimental results show that the posterior regularization can improve the original variational approach to the weakly supervised sentiment analysis and the performance is more stable with smaller prediction variance.