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Register a new userSarcasm and Sentiment Detection in Arabic: investigating the interest of character-level features
السخرية والكشف عن المعنويات باللغة العربية: التحقيق في مصلحة الميزات على مستوى الشخصية
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نقدم ثلاث طرق تم تطويرها للمهمة المشتركة بشأن السخرية والكشف عن المعنويات باللغة العربية.نقدم خط الأساس الذي يستخدم ميزات شخصية N-Gram.نقترح أيضا طريقتين أكثر تطورا: شبكة عصبية متكررة مع تمثيل مستوى الكلمة وتصنيف الفرقة تعتمد على ميزات Word ومستوى الأحرف.لقد اخترنا تقديم نتائج من مصنف الفرقة، لكن لم يكن ناجحا للغاية مقارنة بأفضل النظم: 22/37 بشأن اكتشاف السخرية و 15/22 على اكتشاف المعنويات.لقد بدا أخيرا أن خط الأساس لدينا قد تم تحسينه وتغلب على تلك النتائج.
We present three methods developed for the Shared Task on Sarcasm and Sentiment Detection in Arabic. We present a baseline that uses character n-gram features. We also propose two more sophisticated methods: a recurrent neural network with a word level representation and an ensemble classifier relying on word and character-level features. We chose to present results from an ensemble classifier but it was not very successful as compared to the best systems : 22th/37 on sarcasm detection and 15th/22 on sentiment detection. It finally appeared that our baseline could have been improved and beat those results.
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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 w
e 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.
We describe our submitted system to the 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic (Abu Farha et al., 2021). We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). We used state-of-the-ar
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Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of
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