A Multiclass Depression Detection in Social Media Based on Sentiment Analysis

Mustafa, Raza Ul; Ashraf, Noman; Ahmed, Fahad Shabbir; Ferzund, Javed; Shahzad, Basit; Gelbukh, Alexander

doi:10.1007/978-3-030-43020-7_89

Cited by 44 publications

(15 citation statements)

References 14 publications

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“…Kim et al [20] used XGBoost and convolutional neural networks (CNN) to analyze texts from Reddit to determine whether a user has depression, anxiety, or a personality disorder. Mustafa et al [21] utilize the fourteen psychological attributes in Linguistic Inquiry and Word Count (LIWC) to analyze emotions and identify depression. Based on the weights assigned by LIWC, a machine learning classifier was trained to classify users into three categories of depression.…”

Section: Depression Detectionmentioning

confidence: 99%

Attention-Based Bi-LSTM Model for Arabic Depression Classification

Almars¹

2022

Computers, Materials &Amp; Continua

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Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity of Arabic language and the lack of resources and techniques available. In this study, we performed a depression analysis on Arabic social media content to understand the feelings of the users. A bidirectional long short-term memory (Bi-LSTM) with an attention mechanism is presented to learn important hidden features for depression detection successfully. The proposed deep learning model combines an attention mechanism with a Bi-LSTM to simultaneously focus on discriminative features and learn significant word weights that contribute highly to depression detection. In order to evaluate our model, we collected a Twitter dataset of approximately 6000 tweets. The data labelling was done by manually classifying tweets as depressed or not depressed. Experimental results showed that the proposed model outperformed state-of-the-art machine learning models in detecting depression. The attention-based Bi-LSTM model achieved 0.83% accuracy on the depression detection task.

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Section: Depression Detectionmentioning

confidence: 99%

Attention-Based Bi-LSTM Model for Arabic Depression Classification

Almars¹

2022

Computers, Materials &Amp; Continua

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“…However, such results cannot be directly applied to user-level depression detection, or it may lead to an incorrect prediction. Second, in several existing studies [1,19,[22][23][24], the size of the dataset used for modeling is insufficient, with only a few hundred to a few thousand data samples being used. Because of the difficulty of accurately obtaining and labeling depressed samples, researchers usually choose to construct small datasets or directly cited datasets from other works.…”

Section: Challengesmentioning

confidence: 99%

Section: Detection Approaches Based On Traditional Machine Learningmentioning

confidence: 99%

“…In recent years, more TML-based work has begun to emerge [21,23,24]. In particular, Mustafa et al [23] implemented Frequency-Inverse Document Frequency (TF-IDF) algorithm to weight the words in tweets. Their trained classifier based on an one-dimensional convolution neural network (CNN-1D) achieved an F1-Score of 0.89.…”

Section: Detection Approaches Based On Traditional Machine Learningmentioning

confidence: 99%

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A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo

Wang¹,

Wang²,

Li³

et al. 2020

Preprint

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In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly. The online social network (OSN) provides researchers with another perspective for detecting individuals suffering from depression. However, existing studies of depression detection based on machine learning still leave relatively low classification performance, suggesting that there is significant improvement potential for improvement in their feature engineering. In this paper, we manually build a large dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community), namely Weibo User Depression Detection Dataset (WU3D). It includes more than 20,000 normal users and more than 10,000 depressed users, both of which are manually labeled and rechecked by professionals. By analyzing the user's text, social behavior, and posted pictures, ten statistical features are concluded and proposed. In the meantime, text-based word features are extracted using the popular pretrained model XLNet. Moreover, a novel deep neural network classification model, i.e. FusionNet (FN), is proposed and simultaneously trained with the above-extracted features, which are seen as multiple classification tasks. The experimental results show that FusionNet achieves the highest F1-Score of 0.9772 on the test dataset. Compared to existing studies, our proposed method has better classification performance and robustness for unbalanced training samples. Our work also provides a new way to detect depression on other OSN platforms.

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“…Duggan (2017) reported that a large number of users on social media have experienced abusive behavior, or have observed cases of harassment directed to other fellows. Research has shown that these events not only lead to mental stress and anxiety in users but, in some cases, individuals end up shutting down their social media accounts and, in extreme cases, even causes individuals to take their own lives (Hinduja & Patchin, 2010;Ashraf et al, 2020;Mustafa et al, 2020). The severity of the consequences of online abuse urges the need to research the development of abusive language detection models (Yin & Zubiaga, 2021).…”

Section: Introductionmentioning

confidence: 99%

Abusive language detection in youtube comments leveraging replies as conversational context

Ashraf

Zubiaga

Gelbukh

2021

PeerJ Computer Science

Self Cite

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Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.

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A Multiclass Depression Detection in Social Media Based on Sentiment Analysis

Cited by 44 publications

References 14 publications

Attention-Based Bi-LSTM Model for Arabic Depression Classification

Attention-Based Bi-LSTM Model for Arabic Depression Classification

A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo

Abusive language detection in youtube comments leveraging replies as conversational context

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