Irony-and-Sarcasm-detection-in-Arabic-tweets

This repo represents model developed for Irony and sentiment detection in Arabic tweets in WANLP shared tasks on sarcasm and sentiment detection in Arabic

Overview

This github is an implementation for accepted manuscript titled WANLP 2021 Shared Task: Towards Irony and Sentiment detection in Arabic tweets using Multi-headed-LSTM-CNN-GRU and MARBERT.

Publised paper.

If you find code/work useful, please consider citing

@inproceedings{abdel-salam-2021-wanlp,
    title = "{WANLP} 2021 Shared-Task: Towards Irony and Sentiment Detection in {A}rabic Tweets using Multi-headed-{LSTM}-{CNN}-{GRU} and {M}a{RBERT}",
    author = "Abdel-Salam, Reem",
    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.wanlp-1.37",
    pages = "306--311",
    abstract = "Irony and Sentiment detection is important to understand people{'}s behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username {``}rematchka{''}. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.",
}

Irony and Sentiment detection is important to understand people's behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset . In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username rematchka. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.

Results

1. Official Results from website shared task 1
2. Non-Official Results from website shared task 1
3. Official Results from website shared task 2
4. Non-Official Results from website shared task 2