USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings

Bharti Goel, Ravi Sharma

Abstract
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 non-targeted 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 short-term 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.
Anthology ID:
S19-2139
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
796–800
Language:
URL:
https://aclanthology.org/S19-2139
DOI:
10.18653/v1/S19-2139
Bibkey:
Cite (ACL):
Bharti Goel and Ravi Sharma. 2019. USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 796–800, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings (Goel & Sharma, SemEval 2019)
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PDF:
https://preview.aclanthology.org/autopr/S19-2139.pdf
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