Twitter Data set for Arabic Sentiment Analysis

Donated on 4/10/2014

This problem of Sentiment Analysis (SA) has been studied well on the English language but not Arabic one. Two main approaches have been devised: corpus-based and lexicon-based.

Dataset Characteristics


Subject Area

Social Science

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional Information

--- By using a tweet crawler, we collect 2000 labelled tweets (1000 positive tweets and 1000 negative ones) on various topics such as: politics and arts. These tweets include opinions written in both Modern Standard Arabic (MSA) and the Jordanian dialect. --- The selected tweets convey some kind of feelings (positive or negative) and the objective of our model is to extract valuable information from such tweets in order to determine the sentiment orientation of the inputted text. The months-long annotation process of the tweets is manually conducted mainly by two human experts (native speakers of Arabic). If both experts agree on the label of a certain tweet, then the tweet is assigned this label. Otherwise, a third expert is consulted to break the tie. --- Predicted attribute: class of opinion polarity.

Has Missing Values?


Variable Information

1. Tweet as a string vector 2. class: -- Positive polarity -- Negative poalrity Summary Statistics: Positive Negative Total tweets 1000 1000 Total words 7189 9769 Avg. words in each tweet 7.19 9.97 Avg. characters in each tweet 40.04 59.02


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N. Abdulla


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