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 with no code
Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis
There is a need to develop ASA tools that can be used in industry, as well as in academia, for Arabic text SA.
Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter
We proposed a BERT based weighted ensemble learning approach to integrate the advantages of various BERT models in dialectal Arabic speech acts classification.
Arabic Sentiment Analysis with Noisy Deep Explainable Model
This paper proposes an explainable sentiment classification framework for the Arabic language by introducing a noise layer on Bi-Directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome over-fitting problem.
Multilevel sentiment analysis in arabic
According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language.
Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis
Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model.
Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST
From our recently released ASAD dataset, we provide the competitors with 55K tweets for training, and 20K tweets for validation, based on which the performance of participating teams are ranked on a leaderboard, https://www. kaggle. com/c/arabic-sentiment-analysis-2021-kaust.
Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic
In this paper, the author addresses the negation problem of machine learning-based sentiment classification for a colloquial Arabic language.
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA).
Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance
Many works have been done for Arabic sentiment analysis based on word embedding, but there is no study focused on variable parameters.
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively.
