6 research outputs found
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
Data annotation is an important but time-consuming and costly procedure. To
sort a text into two classes, the very first thing we need is a good annotation
guideline, establishing what is required to qualify for each class. In the
literature, the difficulties associated with an appropriate data annotation has
been underestimated. In this paper, we present a novel approach to
automatically construct an annotated sentiment corpus for Algerian dialect (a
Maghrebi Arabic dialect). The construction of this corpus is based on an
Algerian sentiment lexicon that is also constructed automatically. The
presented work deals with the two widely used scripts on Arabic social media:
Arabic and Arabizi. The proposed approach automatically constructs a sentiment
corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to
Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi
test sets, respectively. Ongoing work is aimed at integrating transliteration
process for Arabizi messages to further improve the obtained results.Comment: To appear in the 9th International Conference on Brain Inspired
Cognitive Systems (BICS 2018
An Empirical Evaluation of Arabic-Specific Embeddings for Sentiment Analysis
International audienc