Related Tasks can Share! A Multi-task Framework for Affective language

TL;DR

This paper introduces a multi-task learning framework that jointly models sentiment classification and intensity prediction, leveraging their relatedness to improve performance using a convolutional-GRU model with handcrafted features.

Contribution

It presents a novel multi-task framework combining sentiment classification and intensity prediction with a convolutional-GRU model and handcrafted features, outperforming single-task models.

Findings

Joint learning outperforms single-task models.

Multi-task framework improves sentiment analysis accuracy.

Handcrafted features enhance model performance.

Abstract

Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks.