USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings
Bharti Goel, Ravi Sharma · 2019
In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team.For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A).In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or nontargeted like swearing (Sub-task B).In targeted tweets, our system identifies the target as an individual or group (Sub-task C).We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary.Later, we used keras tokenizer and word embeddings for feature extraction.For classification, we used the LSTM (Long shortterm memory) model of keras framework.Our accuracy scores for Sub-task A, B and C are 0.8128, 0.8167 and 0.3662 respectively.Our results indicate that fine-grained classification to identify offense target was difficult for the system.Lastly, in the future scope section, we will discuss the ways to improve system performance.