Gender identification for Egyptian Arabic dialect in twitter using deep learning models

Elsayed, Shereen; Farouk, Mona

doi:10.1016/j.eij.2020.04.001

Cited by 17 publications

(18 citation statements)

References 5 publications

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“…Although most researchers tend to develop solutions to the English text, several research works have been proposed to handle other languages such as Arabic [31][32][33], Russian [34,35], and Portuguese [36]. The authors of [32] have introduced a language-specific algorithm based on N-Gram Feature Vector that deals with Egyptian dialect.…”

Section: Gender Identification Techniquesmentioning

confidence: 99%

“…Feature weights were applied using the Random Forest with Mixed Feature Vector and Logistic Regression with N-Gram Feature Vector. Further, in 2020 [33], the authors employed deep learning techniques for handling the gender identification task. They have investigated several Neural Network varieties (e.g., Convolutional Neural Networks, Convolutional Bidirectional Long-Short Term Memory, Long-Short Term Memory) on Arabic text on Twitter, more specifically, Egyptian dialect.…”

Section: Gender Identification Techniquesmentioning

confidence: 99%

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Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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Section: Gender Identification Techniquesmentioning

confidence: 99%

Section: Gender Identification Techniquesmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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“…Predicting Gender from Profile Images. To evaluate the efficiency of using tools that detect gender from profile images, we user Gender-and-Age-Detection tool 11 on ARAP test set. It uses deep learning to identify the gender and age of a person from face image, in which model was trained on ∼27K images from Flickr (Adience dataset) [16].…”

Section: Additional Experimentsmentioning

confidence: 99%

“…Malmasi [20] use first names to classify the gender of Arabic, German, Iranian and Japanese names. ElSayed and Farouk [11] uses neural networks to differentiate male and female authors of tweets in Egyptian dialect. Hussein et al [15] use classical machine learning classifiers such as Logistic Regression and Random Forest classifiers to identify gender in Egyptian tweets.…”

Section: Introductionmentioning

confidence: 99%

ArabGend: Gender Analysis and Inference on Arabic Twitter

Mubarak¹,

Chowdhury²,

Alam³

2022

Preprint

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Gender analysis of Twitter can reveal important socio-cultural differences between male and female users. There has been a significant effort to analyze and automatically infer gender in the past for most widely spoken languages' content, however, to our knowledge very limited work has been done for Arabic. In this paper, we perform an extensive analysis of differences between male and female users on the Arabic Twitter-sphere. We study differences in user engagement, topics of interest, and the gender gap in professions. Along with gender analysis, we also propose a method to infer gender by utilizing usernames, profile pictures, tweets, and networks of friends. In order to do so, we manually annotated gender and locations for ∼166K Twitter accounts associated with ∼92K user location, which we plan to make publicly available. 1 Our proposed gender inference method achieve an F1 score of 82.1%, which is 47.3% higher than majority baseline. In addition, we also developed a demo and made it publicly available. CCS CONCEPTS• Computing methodologies → Natural language processing; Machine learning approaches; Artificial intelligence.

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“…Gender Detection: Gender detection from various types of data including images and text on social media such as Facebook, Twitter and even from different language and dialects has been extensively studied using deep learning models (ElSayed and Farouk, 2020). Akbulut et al (2017) utilized knowledge from local receptive fields and CNNs to identify the gender of a person from their faces.…”

Section: Related Workmentioning

confidence: 99%

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

Lokala¹,

Srivastava²,

Dastidar³

et al. 2022

Preprint

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Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel taskadaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledgeassisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models 1 . Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.

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Gender identification for Egyptian Arabic dialect in twitter using deep learning models

Cited by 17 publications

References 5 publications

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

ArabGend: Gender Analysis and Inference on Arabic Twitter

A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

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