Arabic Sentiment Analysis

LABR is a large sentiment analysis dataset to-date for the Arabic language. It consists of over 63,000 book reviews, each rated on a scale of 1 to 5 stars. Source: LABR: A Large Scale Arabic Book Reviews Dataset

The Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV) is a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Source: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets

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. Addressing this gap, we present ArSen-20, a benchmark dataset tailored to propel Arabic sentiment detection forward. ArSen-20 comprises 20,000 professionally labeled tweets sourced from Twitter, focusing on the theme of COVID-19 and spanning the period from 2020 to 2023. Beyond tweet content, the dataset incorporates metadata associated with the user, enriching the contextual understanding. ArSen-20 offers a comprehensive resource to foster advancements in Arabic sentiment analysis and facilitate research in this critical domain. The ArSen-20 dataset statistics: | Statistics | Num | |:-------------:|:-----:| | Training set size | 16000 | | Validation set size| 2000 | | Testing set size | 2000 | | Neutral | 17262 | | Positive | 878 | | Negative | 1860 | ## Features The dataset has the following features: | Field | Type | Description | |:-----------:| :--------: |:----------------: | | tweet id | string | The unique identifier of the requested Tweet. | | label | string | Sentiment Classification of this tweet. | | author id | string |The unique identifier of this user. | | created_at | data | Creation time of the Tweet. | | lang | string | Language of the Tweet, if detected by Twitter. | | like_count | int |The number of likes on this tweet.| |quote_count | int | The number of times this tweet has been quoted. | | reply_count | int | The number of replies to this tweet. | | retweet_count| int | The number of retweets to this tweet. | | tweet | string | The actual UTF-8 text of the Tweet. | |user_verified | boolean | Indicates if this user is a verified Twitter User. | |followers_count | int |The number of followers of the author. | | following_count | int | The number of following of the author. | | tweet_count | int | Total number of tweets by the author. | | listed_count | int |The number of public lists that this user is a member of. | |name | string | The name of the user. | | username | string | The Twitter screen name, handle, or alias. | | user_created_at| data | The UTC datetime that the user account was created. | | description | string | The text of this user’s profile description (bio). | ## DownLoad You can download the dataset from here. + ArSen-20_publish.csv - Contains all features. + ArSen-20_id_only.csv - Contains only tweets and their author's id. ## Citation If you use this dataset in your research, please cite the following papers: bibtex @inproceedings{fang2024arsen, title={ArSen-20: A New Benchmark for Arabic Sentiment Detection}, author={Yang Fang and Cheng Xu}, booktitle={5th Workshop on African Natural Language Processing}, year={2024}, url={https://openreview.net/forum?id=GgsRUF5kJt} } bibtex @inproceedings{fang2024advancing, title = "Advancing {A}rabic Sentiment Analysis: {A}r{S}en Benchmark and the Improved Fuzzy Deep Hybrid Network", author = "Fang, Yang and Xu, Cheng and Guan, Shuhao and Yan, Nan and Mei, Yuke", editor = "Barak, Libby and Alikhani, Malihe", booktitle = "Proceedings of the 28th Conference on Computational Natural Language Learning", month = nov, year = "2024", address = "Miami, FL, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.conll-1.39", pages = "507--516", } ## contact If you have any questions or comments about the dataset, please contact Yang Fang (20211209024@chnu.edu.cn). Potential cooperation in related fields is also welcome. :)

Sentiment analysis is pivotal in Natural Language Processing for understanding opinions and emotions in text. While advancements in Sentiment analysis for English are notable, Arabic Sentiment Analysis (ASA) lags, despite the growing Arabic online user base. Existing ASA benchmarks are often outdated and lack comprehensive evaluation capabilities for state-of-the-art models. To bridge this gap, we introduce ArSen, a meticulously annotated COVID-19-themed Arabic dataset, and the IFDHN, a novel model incorporating fuzzy logic for enhanced sentiment classification. ArSen provides a contemporary, robust benchmark, and IFDHN achieves state-of-the-art performance on ASA tasks. Comprehensive evaluations demonstrate the efficacy of IFDHN using the ArSen dataset, highlighting future research directions in ASA.