# Sentiment and Sarcasm Classification with Multitask Learning

## TL;DR

This paper introduces a multi-task learning framework that jointly models sentiment analysis and sarcasm detection, leveraging their correlation to improve performance on both tasks in NLP.

## Contribution

It presents a novel deep neural network-based multi-task learning approach that outperforms existing methods by 3-4% on benchmark datasets.

## Key findings

- Multi-task learning improves sentiment and sarcasm detection accuracy.
- The proposed model outperforms state-of-the-art methods by 3-4%.
- Sentiment and sarcasm detection are correlated and benefit from joint modeling.

## Abstract

Sentiment classification and sarcasm detection are both important natural language processing (NLP) tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multi-task learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting. Our method outperforms the state of the art by 3-4% in the benchmark dataset.