Garain at SemEval-2020 Task 12: Sequence based Deep Learning for Categorizing Offensive Language in Social Media

TL;DR

This paper presents a deep learning approach using LSTMs and sequence features for offensive language target identification in social media, achieving nearly 48% F1 score on a multilingual dataset.

Contribution

The paper introduces a sequence-based deep learning system combining automatic and manual features for offensive target identification in social media.

Findings

Achieved a macro F1 score of 47.76% on the dataset.

Utilized LSTMs with combined feature sets for improved classification.

Demonstrated effectiveness of sequence features in offensive language detection.

Abstract

SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). The task was subdivided into multiple languages and datasets were provided for each one. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification. I have participated in the task-C, that is, offense target identification. For preparing the proposed system, I have made use of Deep Learning networks like LSTMs and frameworks like Keras which combine the bag of words model with automatically generated sequence based features and manually extracted features from the given dataset. My system on training on 25% of the whole dataset achieves macro averaged f1 score of 47.763%.