Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Musleh, Dhiaa; Alkhales, Taef A.; AlMakki, Reem; AlNajim, Shahad; Almarshad, Shaden K.; Alhasaniah, Rana S.; Aljameel, Sumayh S.; Almuqhim, Abdullah A.

doi:10.32604/cmc.2022.022508

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…They used the same data to train models with large, base, medium, and minimum sizes for 4M stages, shown in Table 1. Similar studies have been proposed in [15][16][17], where the authors investigated the effects of COVID-19 over the mental health using Arabic Tweets analysis in particular.…”

Section: Related Workmentioning

confidence: 62%

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Alotaibi¹,

Rahman²,

Alhaza³

et al. 2022

MMEP

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Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural Language Processing (NLP). NLP is mainly investigated in the English language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature.

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Section: Related Workmentioning

confidence: 62%

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Alotaibi¹,

Rahman²,

Alhaza³

et al. 2022

MMEP

View full text Add to dashboard Cite

show abstract

“…In this regard, much research has been conducted in order to understand the statements expressed through tweets and to classify them into positive and negative sentiments while taking into account certain parameters (e.g., population, language, etc.). Traditional approaches used classic machine learning algorithms such as decision trees and SVMs (support vector machines) (see for instance [3][4][5][6][7][8][9]). However, as the data volumes have become very large, recent research has shifted towards deep learning techniques such as recurrent neural networks (RNN) and convolutional neural networks (CNN) (see for example [10,11]).…”

Section: Detection Of Depression and Anxiety Disorders On The Twitter...mentioning

confidence: 99%

“…Much research has been conducted on the detection of depressive and anxiety mental disorders through social media platforms [3][4][5][6][7][8][9][10][11], in particular using Twitter, while considering different factors such as population, period, language, etc. Most of such studies rely on supervised machine learning models for text classification using either traditional learning techniques such as SVM, RF, NB and LR or deep learning approaches such as RNN, LSTM, GRU, Bi_RNN, Bi_LSTM and Bi_GRU.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data

Bendebane,

Laboudi,

Saighi

et al. 2023

Algorithms

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Social media occupies an important place in people’s daily lives where users share various contents and topics such as thoughts, experiences, events and feelings. The massive use of social media has led to the generation of huge volumes of data. These data constitute a treasure trove, allowing the extraction of high volumes of relevant information particularly by involving deep learning techniques. Based on this context, various research studies have been carried out with the aim of studying the detection of mental disorders, notably depression and anxiety, through the analysis of data extracted from the Twitter platform. However, although these studies were able to achieve very satisfactory results, they nevertheless relied mainly on binary classification models by treating each mental disorder separately. Indeed, it would be better if we managed to develop systems capable of dealing with several mental disorders at the same time. To address this point, we propose a well-defined methodology involving the use of deep learning to develop effective multi-class models for detecting both depression and anxiety disorders through the analysis of tweets. The idea consists in testing a large number of deep learning models ranging from simple to hybrid variants to examine their strengths and weaknesses. Moreover, we involve the grid search technique to help find suitable values for the learning rate hyper-parameter due to its importance in training models. Our work is validated through several experiments and comparisons by considering various datasets and other binary classification models. The aim is to show the effectiveness of both the assumptions used to collect the data and the use of multi-class models rather than binary class models. Overall, the results obtained are satisfactory and very competitive compared to related works.

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“…A similar study was conducted to aid in the diagnosis of depression in [46]. After collecting and thoroughly analyzing 4542 tweets based on nine depression symptoms, the tweets were classified into three broad sentiment categories: "non-depressed", "depressed", and "neutral".…”

Section: Background and Related Workmentioning

confidence: 99%

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Boulesnane¹,

Meshoul

Aouissi³

2022

Mathematics

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The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. The obvious relationship between the number of infectious disease cases and the number of social media posts prompted us to consider how we can leverage such health-related content to detect the emergence of diseases, particularly influenza-like illnesses, and foster disease surveillance systems. We used Algerian Arabic posts as a case study in our research. From data collection to content classification, a complete workflow was implemented. The main contributions of this work are the creation of a large corpus of Arabic Facebook posts based on Algerian dialect and the proposal of a new classification model based on sentiment analysis and one-dimensional convolutional neural networks. The proposed model categorizes Facebook posts based on the users’ feelings. To counteract data imbalance, two techniques have been considered, namely, SMOTE and random oversampling (ROS). Using a 5-fold cross-validation, the proposed model outperformed other baseline and state-of-the-art models such as SVM, LSTM, GRU, and BiLTSM in terms of several performance metrics.

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Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Cited by 20 publications

References 14 publications

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

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