PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags

TL;DR

This paper presents a system that leverages emotion-related knowledge from emojis and hashtags in tweets, using neural network features with traditional classifiers to improve emotion detection across five subtasks.

Contribution

The novel approach combines large distantly labeled corpora with neural network feature extraction and traditional classifiers for emotion analysis in tweets.

Findings

Placed among the Top 3 in all participated subtasks

Effective transfer of emotion knowledge from emojis and hashtags

Utilized neural network features with traditional ML models

Abstract

This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.