# Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying

## TL;DR

This paper introduces a lifelong learning approach for sentiment classification that reduces manual data annotation and emphasizes self-study, enabling models to continually learn and reuse knowledge for improved performance.

## Contribution

It proposes a semi-unsupervised lifelong learning framework that minimizes labeled data needs and enhances knowledge accumulation for sentiment analysis tasks.

## Key findings

- Achieves comparable or better performance with less labeled data
- Reduces manual annotation effort significantly
- Demonstrates effective knowledge reuse across tasks

## Abstract

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.