SLSA: A Sentiment Lexicon for Standard Arabic

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2545–2550, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. SLSA: A Sentiment Lexicon for Standard Arabic Ramy Eskander and Owen Rambow Center for Computational Learning Systems Columbia University {rnd2110,ocr2101}@columbia.edu Abstract Sentiment analysis has been a major area of interest, for which the existence of high- quality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Ara- bic Sentiment Lexicon (SLSA) that is pub- licly available for free and avoids the de- ficiencies in the current resources. SLSA has the highest up-to-date reported cover- age. 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-the- art lexicon when tested for accuracy. also outperforms it by an absolute 3.5% ofIt F1-score when tested for sentiment analy- sis. 1 Introduction Sentiment analysis is the process of identifying and extracting subjective information using Nat- ural Language Processing (NLP). It helps identi- fying opinions and extracting relevant information that lies behind the analyzed data. Sentiment anal- ysis has received enormous interest in NLP, and in particular in the context of web content. This includes social media, blogs, discussions, reviews and advertisement. While there has been extensive work on senti- ment analysis in English and other languages of interest, less work has been done for Arabic. A major concern in Arabic NLP is the morphological complexity of the language along with the limited number of resources, corpora in particular. The goal of this work is to build a publicly available large-scale Sentiment Lexicon for Stan- dard Arabic (SLSA). For every lemma and part- of-speech (POS) combination that exists in a large Standard Arabic lexicon, SLSA assigns the scores of three sentiment labels: positive, negative and objective, in addition to the English gloss. The positive and negative scores range between zero and one, while the objective score is defined as 1 - (positive score + negative score). The existence of SLSA is valuable to the field of Arabic sentiment analysis, which is expected to receive considerable focus during the current decade. SLSA is the first sentiment lexicon for Arabic to combine the following four strengths. High coverage SLSA lists the sentiment of about 35,000 lemma and POS combinations, which is the highest coverage reported for Standard Arabic sentiment lexicons. High quality Unlike many of the current lexicons whose construction is based on semi-supervised learning and heuristic-based approaches, SLSA is not constructed via machine learning models, while the use of heuristics is minimal. Richness As opposed to sparse surface-based lex- icons, SLSA is a lemma-based resource that at- taches POS and English gloss information to each lemma, where the information of a lemma is appli- cable to its inflected forms. This makes the lexicon more useful when used by other research. Public Availability SLSA is based …

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