Lifelong Learning for Sentiment Classification

TL;DR

This paper introduces a lifelong learning method for sentiment classification that retains past knowledge and improves future learning, using a Bayesian optimization framework with stochastic gradient descent, showing significant performance gains.

Contribution

It presents a novel lifelong learning approach specifically tailored for sentiment classification, integrating Bayesian optimization with stochastic gradient descent.

Findings

Outperforms baseline methods significantly

Demonstrates the effectiveness of lifelong learning in sentiment analysis

Shows potential for lifelong learning as a promising research direction

Abstract

This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction.