AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

Al-Laith, Ali; Shahbaz, Muhammad; Alaskar, Hind; Rehmat, Asim

doi:10.3390/app11052434

Cited by 30 publications

(24 citation statements)

References 37 publications

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“…Text classification using machine/deep learning provides a good results over many NLP applications including, sentiment analysis [30], [31], emotion detection [32], hate speech detection [33], sarcasm detection [34], and other applications.…”

Section: Related Workmentioning

confidence: 99%

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Mahlous¹,

Al-Laith²

2021

IJACSA

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In March 2020, the World Health Organization declared the COVID-19 outbreak to be a pandemic. Soon afterwards, people began sharing millions of posts on social media without considering their reliability and truthfulness. While there has been extensive research on COVID-19 in the English language, there is a lack of research on the subject in Arabic. In this paper, we address the problem of detecting fake news surrounding COVID-19 in Arabic tweets. We collected more than seven million Arabic tweets related to the corona virus pandemic from January 2020 to August 2020 using the trending hashtags during the time of pandemic. We relied on two fact-checkers: the France-Press Agency and the Saudi Anti-Rumors Authority to extract a list of keywords related to the misinformation and fake news topics. A small corpus was extracted from the collected tweets and manually annotated into fake or genuine classes. We used a set of features extracted from tweet contents to train a set of machine learning classifiers. The manually annotated corpus was used as a baseline to build a system for automatically detecting fake news from Arabic text. Classification of the manually annotated dataset achieved an F1-score of 87.8% using Logistic Regression (LR) as a classifier with the n-gram-level Term Frequency-Inverse Document Frequency (TF-IDF) as a feature, and a 93.3% F1score on the automatically annotated dataset using the same classifier with count vector feature. The introduced system and datasets could help governments, decision-makers, and the public judge the credibility of information published on social media during the COVID-19 pandemic.

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Section: Related Workmentioning

confidence: 99%

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Mahlous¹,

Al-Laith²

2021

IJACSA

Self Cite

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“…A sentiment annotation of a text involves labeling the text based on the sentiment presented in that text. Three approaches can be utilized for sentiment annotation: automatic annotation, semi-automatic annotation, and manual annotation [41]. In this research paper, the constructed dataset was manually annotated by three annotators.…”

Section: Annotating the Datasetmentioning

confidence: 99%

Sentiment Analysis of Emirati Dialect

Shamsi

Abdallah

2022

BDCC

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Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.

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“…In [22], authors have used sentiment and emotion classification to determine the avalanche point of an epidemic outbreak. The common thing in many of the recent studies regarding sentiment analysis is the use of Twitter as the primary source of data [23] [24] [25]. It is owing to the fact that Twitter is a universal microblogging website and it allows the users to express their thoughts in limited characters which makes the preprocessing part easy for the researchers [26].…”

Section: Related Workmentioning

confidence: 99%

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

et al. 2021

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Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes' (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.

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AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

Cited by 30 publications

References 37 publications

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Sentiment Analysis of Emirati Dialect

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

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