Arabic Sentiment Analysis
5 papers with code • 0 benchmarks • 2 datasets
Arabic sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of arabic text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Benchmarks
These leaderboards are used to track progress in Arabic Sentiment Analysis
LABR
Most implemented papers
LABR: A Large Scale Arabic Sentiment Analysis Benchmark
We explore using the dataset for two tasks: (1) sentiment polarity classification; and (2) ratings classification.
A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine
anastasialavrova/bmstu_bachelor_qualification_work
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In contrast to the dialectal Arabic language, these selection methods have been investigated widely for the English language.
hULMonA: The Universal Language Model in Arabic
Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
CAMeL-Lab/camel_tools
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LREC 2020
We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python.
A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets
This SCM+MMA model is applied to SudSenti2 and SudSenti3 with accuracies of 92. 75% and 84. 39%.
