Arabic Sentiment Analysis
6 papers with code • 0 benchmarks • 3 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
Latest papers
ArSen-20: A New Benchmark for Arabic Sentiment Detection
Sentiment detection remains a pivotal task in natural language processing, yet its development in Arabic lags due to a scarcity of training materials compared to English.
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%.
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.
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.
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.
LABR: A Large Scale Arabic Sentiment Analysis Benchmark
We explore using the dataset for two tasks: (1) sentiment polarity classification; and (2) ratings classification.
