Name: SenZi
Description: by translating, annotating, and transliterating
  other resources to have an initial set of 2K sentiment words. We expanded it to 24.6K sentiment
  words by importing inflectional and orthographic
  forms using word embeddings
Provider: Multiple institutions
Paper Title: SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)
  
Tasks: sentiment analysis, transliteration
Venue Name: Recent Advances in Natural Language Processing
Authors: Taha Tobaili,Miriam Fernández,Harith Alani,S. Sharafeddine,Hazem M. Hajj,Goran Glavas
Abstract: Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.