Identifying Depression Among Twitter Users using Sentiment Analysis

Azam, Fiza; Sami, Memoona; Abro, Muhammad Hassan; Dewani, Amirita

doi:10.1109/icai52203.2021.9445271

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…This [13] study was diagnostic criteria are based on patientreported symptoms and have implications for patient management; Therefore, alternative methods should be investigated. Social media platforms like Facebook, Twitter, Reedit, and Tumbler offer new ways to collect behavioral data that can reveal insights into a user's emotional state.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

Abbas,

Munir,

Raza

et al. 2024

IEEE Access

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Depression constitutes a significant mental health condition, impacting an individual's emotional state, thought processes, and ability to carry out everyday tasks. Depression is defined by ongoing feelings of sadness, diminished interest in previously enjoyed activities, alterations in hunger, sleep disturbances, decreased vitality, and challenges with focus. The impact of depression extends beyond the individual, affecting society at large through decreased productivity and higher healthcare costs. In the realm of social media, users often express their thoughts and emotions through posts, which can provide insightful data for identifying patterns of depression. This research aims to detect depression early by analyzing social media user content with machine learning techniques. We have built advanced machine learning models using a benchmark depression database containing 20,000 tagged tweets from user profiles identified as depressed or non-depressed. We are introducing an innovative BERT-RF feature engineering method that extracts Contextualized Embeddings and Probabilistic Features from textual input. The Bidirectional Encoder Representations from Transformers (BERT) model, based on the Transformer architecture, is used to extract Contextualized Embedding features. These features are then fed into a random forest model to generate class probabilistic features. These prominent features aid in enhancing the identification of depression from social media. In order to classify tweets using the features derived from the BERT-RF features selection step, we have used five popular classifiers: Random Forest (RF), Multilayer Perceptron (MLP), K-Neighbors Classifier (KNC), Logistic Regression (LR), and Long Short-Term Memory (LSTM). Evaluation experiments show that our approach, using BERT-RF for feature engineering, enables the Logistic Regression model to outperform state-of-the-art methods with a high accuracy score of 99%. We have validated the results through k-fold cross-validation and statistical T-tests. We achieved 99% k-fold accuracy during the validation of the proposed approach. This research contributes significantly to computational linguistics and mental health analytics by providing a robust approach to the early detection of user depression from social media content.

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Section: Literature Reviewmentioning

confidence: 99%

“…Performance Score [31] DistilBert 89% [13] Random forest algorithm 77% [16] logistic regression classifier 86% [14] TF-IDF Apporach 89% [32] Convolutional Neural Network(CNN) 94% Our BERT-RF Logistic Regression 99%…”

Section: Ref Proposed Approachmentioning

confidence: 99%

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

Abbas,

Munir,

Raza

et al. 2024

IEEE Access

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“…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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“…However, there are some limitations, the first limitation is using a single traditional machine learning technique and obtaining low accuracy. For example, in [7] two traditional machine learning were applied, which are support vector machine (SVM) and random forest. The best accuracy obtained was 77% with random forest.…”

Section: Introductionmentioning

confidence: 99%

Predicating depression on Twitter using hybrid model BiLSTM-XGBOOST

Kamil,

Abbas

2023

Bulletin EEI

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Nowadays, depression is a common mental illness. Failure to recognize depression early or guarantee that a depressed individual receives prompt counseling can lead to serious issues. Social media allow us to monitor people's thoughts, daily activities, and emotions, including persons with mental illnesses. This study suggested novel hybrid models that combine one of the deep learning techniques with one of the machine learning approaches. This paper used a dataset from the Kaggle website to predict depression. Two deep learning techniques were chosen to conduct the experiments: bidirectional long short-term memory (Bi-LSTM), and convolutional neural network (CNN). Three machine learning techniques were also selected, which are support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBOOST). Deep learning methods were applied to extract important features from input data and training, and then machine learning was utilized to predict the class. The performance of the hybrid models was compared against that of five single models. The results showed that Bi-LSTM-XGBOOST is better than single models and achieve the highest performance, with 94% for all evaluation metrics. The proposed model can improve the performance of machine learning techniques and increase the detection rate of depression.

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Identifying Depression Among Twitter Users using Sentiment Analysis

Cited by 8 publications

References 9 publications

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection From Social Media

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

Predicating depression on Twitter using hybrid model BiLSTM-XGBOOST

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