Year: 2015
Type: conference-paper
Abstract: Sentiment analysis has been a major area of interest, for which the existence of highquality resources is crucial.In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies.The paper presents a large-scale Standard Arabic Sentiment Lexicon (SLSA) that is publicly available for free and avoids the deficiencies in the current resources.SLSA has the highest up-to-date reported coverage.The construction of SLSA is based on linking the lexicon of AraMorph with Sen-tiWordNet along with a few heuristics and powerful back-off.SLSA shows a relative improvement of 37.8% over a state-of-theart lexicon when tested for accuracy.It also outperforms it by an absolute 3.5% of F1-score when tested for sentiment analysis.
DOI: 10.18653/v1/d15-1304
Authors: Ramy Eskander, Owen Rambow
Institution: Columbia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2251526503