Twitter Sentiment Analysis for Arabic Tweets

Abuelenin, Sherihan; Elmougy, Samir; Naguib, Eman

doi:10.1007/978-3-319-64861-3_44

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…They gathered proposed 5 distinct classes for the Islamic apps: Zakat, Qibla/Prayer Time, Quran, Hadith, and Supplications. Abuelenin et al [18] proposed hybrid system to find the most similar word in lexicon and increase the accuracy of Egyptian Arabic using the cosine similarity algorithm and the Information Science Research Institute Arabic stemmer (ISRI).…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Saudy¹,

El-Ghazaly²,

Nasr³

et al. 2022

IJACSA

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Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications. In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution. Our methodology consists of six phases. We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks. To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN). From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN). The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Saudy¹,

El-Ghazaly²,

Nasr³

et al. 2022

IJACSA

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“…ISRI is an Arabic stemmer algorithm that does not use a root dictionary. It is a widely, well-performed stemmer used in the field of Arabic tweets mining [44,45].…”

Section: Data Preprocessingmentioning

confidence: 99%

Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

et al. 2021

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Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

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“…This is important to minimize repetition. The IRSI Arabic stemmer from the NLTK python package is used for stemming the Arabic data [27] (Table 1).…”

Section: Pre-processing Stagementioning

confidence: 99%

Sentiment Analysis of Arabic COVID-19 Tweets

Ahmed

Salloum

Shaalan

2021

Lecture Notes in Networks and Systems

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With the COVID-19 outbreak in 2020, information about the pandemic has been exponentially increasing and spreading across various social media platforms. People across the globe have been affected in a way or another because of different aspects such as the increase in infected cases, death rate increase, financial difficulties, social distancing, being under lockdown, quarantine measures, and working remotely. With people heavily relying on social media platforms to share information more than ever, it is important to analyze their conversations to understand people's sentiments and feelings during this time of crisis to find possible ways to cope with the pandemic. This paper presents a sentiment analysis study to analyze sentiments from Arabic tweets related to COVID-19 using multiple models. After data acquisition, text preprocessing steps are performed and Term Frequency Inverse Document Frequency (TF-IDF) is used to generate feature vectors. Experiments are then done comparing multiple classifiers: Naïve Bayes, Support Vector Machine, Logic Regression, Random Forest, and K-Nearest Neighbor. A comparison of the models' performance was carried out using multiple evaluation metrics including Precision, Accuracy, Recall and F1 Score. The best performing model achieved an accuracy of around 84%.

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Twitter Sentiment Analysis for Arabic Tweets

Cited by 13 publications

References 8 publications

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Sentiment Analysis of Arabic COVID-19 Tweets

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