SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
This addresses the challenge of costly and time-consuming annotation for sentiment analysis in Algerian dialect, an under-resourced domain, though it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of data annotation for sentiment analysis in Algerian dialect by automatically constructing a sentiment corpus of 8000 messages (4000 Arabic, 4000 Arabizi) using an automatically built lexicon, achieving F1-scores of up to 72% for Arabic and 78% for Arabizi test sets.
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.