Shai Fine


2022

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Love Me, Love Me Not: Human-Directed Sentiment Analysis in Arabic
Abraham Israeli | Aviv Naaman | Yotam Nahum | Razan Assi | Shai Fine | Kfir Bar
Proceedings of the Third International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2022) co-located with ICNLSP 2022

2021

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The IDC System for Sentiment Classification and Sarcasm Detection in Arabic
Abraham Israeli | Yotam Nahum | Shai Fine | Kfir Bar
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of social network data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2–an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks.