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2019

Offensive Language Analysis using Deep Learning Architecture

Ong, Ryan

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SemEval-2019 Task 6 (Zampieri et al., 2019b) requires us to identify and categorise offensive language in social media.

In this paper we will describe the process we took to tackle this challenge.
Our process is heavily inspired by Sosa (2017) where he proposed CNN-LSTM and LSTM-CNN models to conduct twitter sentiment analysis.
We decided to follow his approach as well as further his work by testing out different variations of RNN models with CNN.

Offensive Language Analysis using Deep Learning Architecture · Around

Built on

Smote: Synthetic minority over-sampling technique
Original
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2011 · 2011
Earlier work this paper cites.
Twitter sentiment analysis using combined lstm-cnn models
Pedro M. Sosa. 2017 · 2017
Earlier work this paper cites.

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Then

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SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
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Closest in time.

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