Colloquial Arabic Tweets: Collection, Automatic Annotation, and Classification

Khalifa, Yasmin; Elnagar, Ashraf

doi:10.1109/ialp51396.2020.9310507

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Their experiments showed that using SMOTENN improves F1 score for both single and ensemble classifiers where the best result obtained by nuSVM produced an average F1 score value of 99.07. Khalifa and Elnagar [22] focused on studying the performance of their Twitter dataset in its imbalanced and balanced versions using term frequency-inverse document frequency (TF-IDF) and word embeddings. They conducted a comparative evaluation of the gradient boosting, logistic regression (LR), nearest centroid, DT, multinomial NB, SVM, XGBoost (XGB), RF, and AdaBoost classifiers and investigated the performance of the MLP and condensed nearest neighbor (CNN) deep learning classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Al-Khazaleh,

Alian,

Jaradat

2024

Bulletin EEI

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Sentiment analysis is a popular natural language processing task that recognizes the opinions or feelings of a piece of text. Microblogging platforms such as Twitter are a valuable resource for finding such people’s opinions. The majority of Arabic sentiment analysis studies indicated that the data utilized to train machine learning algorithms is balanced. In this paper, we investigated the impact of sampling techniques and classification algorithms on an imbalanced Arabic dataset about people’s perceptions of COVID-19, with the majority of opinions reflecting people’s fear and stress about the pandemic, and the minority reflecting the belief that the pandemic was a hoax. The experiments concentrated on analyzing the imbalanced learning of Arabic sentiments using over-sampling and under-sampling techniques on seven single machine learning algorithms and two common ensemble algorithms from the bagging and boosting families, respectively. Results show that resampling-based approaches can overcome the difficulty of an imbalanced dataset, and the use of over-sampled data leads to better performance than that of under-sampled data. The results also reveal that using oversampled data from synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, or adaptive synthetic sampling with random forest classifier is the most effective in addressing this classification problem, with F1-score value of 0.99.

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Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Al-Khazaleh,

Alian,

Jaradat

2024

Bulletin EEI

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“…They developed their corpus using blog posts and web forums, and then employed it to evaluate the performance of several methods for classifying dialects from a list of 18 countries. Another study relied on the collection of a Twitter dataset from four main regions: the Gulf, the Levantine, North Africa, and Egypt [9]. Another dataset focused on a speech corpus from the Gulf, Egypt, and the Levantine as well as MSA [17].…”

Section: Classification Of Arabic Dialectsmentioning

confidence: 99%

“…Put differently, how do scientists select their unit for defining a dialect? For this, some researchers have focused on datasets divided by regions [7][8][9]. For this specification, a region could be located in one or more countries.…”

Section: Introductionmentioning

confidence: 99%

Similarities between Arabic dialects: Investigating geographical proximity

Alsudais

Alotaibi²,

Alomary³

2022

Information Processing & Management

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“…The use of word embedding models is quite popular in Arabic computational linguistic applications, AL-Smadi et al (2017), Alkhatlan, Kalita, and Alhaddad (2018), Mohamed and Shokry (2022), Bounhas, Soudani, and Slimani (2020). Therefore, measuring the robustness of such embeddings is essential for producing effective, Arabic sentiment analysis, Altowayan and Elnagar (2017), Al-Smadi et al (2019, Khalifa and Elnagar (2020), Nassif et al (2021b), Farha and Magdy (2021), question and answering systems, Romeo et al (2019), Elnagar (2019), clustering, AlMahmoud, Hammo, andFaris (2020), and text classification, Abbas et al (2019), Orabi, El Rifai, and.…”

Section: Literature Reviewmentioning

confidence: 99%

A benchmark for evaluating Arabic word embedding models

2022

Self Cite

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Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.

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Colloquial Arabic Tweets: Collection, Automatic Annotation, and Classification

Cited by 6 publications

References 20 publications

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Similarities between Arabic dialects: Investigating geographical proximity

A benchmark for evaluating Arabic word embedding models

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