Arabic Sentiment Analysis and Cross-lingual Sentiment Resources
This page provides access to Arabic sentiment lexicons and sentiment annotated corpora. English sentiment and emotion lexicons are available here.
The objective of the task is to test an automatic system’s ability to predict a sentiment intensity (aka evaluativeness and sentiment association) score for a word or a phrase. Phrases include negators, modals, intensifiers, and diminishers -- categories known to be challenging for sentiment analysis. Specifically, the participants will be given a list of terms (single words and multiword phrases) and be asked to provide a score between 0 and 1 that is indicative of the term’s strength of association with positive sentiment. A score of 1 indicates maximum association with positive sentiment (or least association with negative sentiment) and a score of 0 indicates least association with positive sentiment (or maximum association with negative sentiment). If a term is more positive than another, then it should have a higher score than the other. There are three tasks corresponding to data from three domains: general English, English Twitter, and Arabic Twitter. The Arabic Twitter Sentiment Lexicon used in SemEval-2016 Task 7 includes single words and phrases commonly found in Arabic tweets. The phrases in this set are formed only by combining a negator and a word. More details can be found in the task website and the associated publication:
Semeval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Svetlana Kiritchenko, Saif M. Mohammad, and Mohammad Salameh. In Proceedings of the International Workshop on Semantic Evaluation (SemEval ’16). June 2016. San Diego, California.
Paper (pdf) BibTeX Presentation Task Website SemEval-2016 Arabic Twitter Sentiment Lexicon
Automatically Created Sentiment Lexicons
These lexicons were created by measuring the extent to which the words in a tweets corpus co-occurred with a set of seed positive and seed negative terms. This is based on the idea that positive terms co-occur more with positive words and less with negative words; and negative words co-occur more with negative words and less with positive words.
Lexicon # positive # negative Seeds Arabic Emoticon Lexicon
A set of twenty three emoticons such as :) and :(. Arabic Hashtag Lexicon
A set of 230 Arabic words that were manually selected for being highly positive or highly negative. Arabic Hashtag Lexicon (dialectal)
A set of 483 dialectal Arabic words compiled by Refaee and Rieser (2014) from tweets.
Lexicons created by translating English Sentiment Lexicons into Arabic:
Translated Lexicon # positive # negative # neutral Original English Lexicon Arabic translation of Bing Liu’s Lexicon
Link to Bing Liu’s Lexicon Arabic translation of MPQA Subjectivity Lexicon
Link to MPQA Subjectivity Lexicon Arabic translation of NRC Emotion Lexicon
8,527 Link to NRC Emotion Lexicon Arabic translation of NRC Emoticon Lexicon 38,312 24,156 - Link to NRC Emoticon Lexicon Arabic translation of NRC Hashtag Sentiment Lexicon 32,048 22,081 - Link to NRC Hashtag Sentiment Lexicon
a. BBN Blog Posts Sentiment Corpus (README):
A random subset of 1200 Levantine dialectal sentences
chosen from the BBN Arabic-Dialect–English Parallel Text. The sentences are extracted social media posts and provided with their translation. Apart from manual translations, the dataset also includes automatic translations into English using the Portage MT system. We manually annotated this subset and its translations (both manual and automatic) for sentiment (positive, negative, or neutral).
This is the first such resource where text in one language and its translations into another language (both manually and automatically produced) are each manually labeled for sentiment.
b. Syria Tweets Sentiment Corpus (README):
A dataset of 2000 tweets originating from Syria (a country where Levantine dialectal Arabic is commonly spoken). These tweets were collected in May 2014 by polling the Twitter API. The dataset also includes automatic translations into English using the Portage MT system. (This dataset is not provided with manual English translation.) We manually annotated this subset and its translations (both manual and automatic) for sentiment (positive, negative, or neutral).
Sentiment Lexicons for Arabic Social Media. Saif M. Mohammad, Mohammad Salameh, and Svetlana Kiritchenko. In Proceedings of the 10th edition of the Language Resources and Evaluation Conference, May 2016, Portorož (Slovenia).
Paper (pdf) BibTeX Presentation
How Translation Alters Sentiment. Saif M. Mohammad, Mohammad Salameh, and Svetlana Kiritchenko,Journal of Artificial Intelligence Research, 2016, Volume 55, pages 95-130.
Paper (pdf) Pre-print version (pdf) BibTeX
Sentiment After Translation: A Case-Study on Arabic Social Media Posts. Mohammad Salameh, Saif M Mohammad and Svetlana Kiritchenko, In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL-2015), June 2015, Denver, Colorado.
Paper (pdf) BibTeX
Q. When we look at the tokens on a browser, there are some tokens like Ø§Ù…Ø§Ù†ØªÙ† which are not meaningful. How can we resolve this?
A. This is an encoding issue that occurs when using a browser. If you are opening the lexicons using a web browser, for example Chrome, just click
on "Customize Chrome button" -> More Tools -> Encoding (and choose UTF8). If you download the lexicons and want to open it using any software (like Notepad++), please make sure to set the encoding correctly. Also make sure to set the encoding to "UTF8" if you open the files using any programming language.
Please let us know if you have any other issues in using the lexicons on that page.
Updated: March, 2016