Sarcasm and Sentiment Detection in A rabic: investigating the interest of character-level features (2021)

Abstract: 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.

## introduction arabic language is one of the most widely spoken language in the world, currently considered as the fifth language @xcite with more than 330 million speakers. it is the official language in more than 22 countries and is therefore an important language to handle for nlp systems. its written form is commonly referred as literary arabic and divided into two categories: classical arabic and modern standard arabic (msa). but arabic has in fact many variants and research effort have been made on tasks like arabic dialects identification @xcite . more finegrained tasks have been tackled by the nlp community and this paper describes our participation to such a task : the shared task on sarcasm and sentiment detection in arabic @xcite . this shared task focuses on analysing tweets and identifying the sentiment (negative, positive or neutral) and whether a tweet is sarcastic or not. more precisely, the task are defined as follows: the paper is organized as follows: section 2, describes the dataset and section 3 is devoted to the methods we developed. section 4 details our results, future directions are given in section 5. ## data the dataset for both tasks is a combination of the arsarcasm @xcite and daict @xcite , we used a 30% subset of the training data to get a dev set. statistics on the resulting data are shown in ## methods developed for both tasks we did not developed a specific method for each task, we rather tried to find features that could be useful for both tasks. we first present a simple baseline (section 3.1) that shows the interest of character-level features. afterwards we develop more sophisticated methods: a simple deep learning approach (section 3.2) and an ensemble classifier taking advantage of both character-level and word-level features (section 3.3). ## conclusion in this paper, we described three systems developed for the shared task on sarcasm and sentiment detection in arabic. we tried to show the interest of combining word-level and character-level features. in order to ease the choice of the type of character n-grams used as features we developed a baseline with a logistic regression classifier using character n-grams. it helped us to find an appropriate combination of features to produce the ensemble method whose results were submitted to this shared task. it appeared that adding tf-idf and class weighting made this baseline better than the ensemble method, showing maybe the interest of digging into character-level features.