SMS-Stylometry

TunBERT is the first release of a pre-trained BERT model for the Tunisian dialect using a Tunisian Common-Crawl-based dataset. TunBERT was applied to three NLP downstream tasks: Sentiment Analysis (SA), Tunisian Dialect Identification (TDI) and Reading Comprehension Question-Answering (RCQA)

Attempt to predict future stock prices based on Google Trends data.

This repository contains the Arabic sarcasm dataset (ArSarcasm)

Topic Mine leverages 1st and/or 2nd party data to identify trending topics and uses our GEMINI to create relevant ads' texts. It generates headlines, descriptions and keywords given a list of products, brands or services and (optionally) additional info about them.

ArSarcasm-v2 is an extension to the original ArSarcasm dataset. It was used for the shared task on sarcasm detection and sentiment analysis, which is a part of WANLP 2021.

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.

Collection of notebooks written while learning to work with Google Trends API.

Geographical Diffusion of IT Trends