# Affect in Tweets Using Experts Model

## TL;DR

This paper introduces the Experts Model, inspired by Mixture of Experts, to improve emotion intensity detection in tweets, outperforming baselines and top competitors in the SemEval-2018 Affect in Tweets task.

## Contribution

The paper proposes a novel Experts Model architecture that effectively captures multiple emotions and their intensities in tweets, advancing emotion detection methods.

## Key findings

- Achieved top-5 results in SemEval-2018 Affect in Tweets task.
- Outperformed baseline and top-performing models.
- Demonstrated effectiveness in detecting multiple emotions with varying intensities.

## Abstract

Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.