We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2).
On various Social Media platforms, people, tend to use the informal way to communicate, or write posts and comments: their local dialects.
Despite their success, most of the available models have been trained on Indo-European languages however similar research for under-represented languages and dialects remains sparse.
Searching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages.
We describe our submitted system to the SemEval 2020.
In this paper, we focus on the Tunisian dialect sentiment analysis used on social media.
On the first stage of COIN, we extract the formal concepts that capture all the cliques and bridges in the social network.
We present in this paper a new approach for the automatic annotation of medical images, using the approach of "bag-of-words" to represent the visual content of the medical image combined with text descriptors based approach tf. idf and reduced by latent semantic to extract the co-occurrence between terms and visual terms.
Moreover, physician, in charge of medical image analysis, might be unavailable at the right time, which may complicate the patient's state.