Depression, a serious mental health disorder, requires accurate classification for effective intervention. Existing methods often fail to capture nuanced emotional and linguistic cues, leading to suboptimal classification of depression severity. This study bridges this gap by leveraging content-based approaches (N-grams) and context-based methods (Sentence Transformers), alongside advanced transformer-based models, to classify mild, moderate, and severe depression using text data sourced from Reddit. By demonstrating the effectiveness of modern NLP techniques in capturing subtle contextual variations, this research highlights the potential of transformer-based models to enhance depression severity detection. The proposed framework offers a scalable and adaptable solution for real-world mental health diagnostics and early intervention systems.

The multi-label emotion classification task aims to identify all possible emotions in a written text that best represent the author’s mental state. In recent years, multi-label emotion classification attracted the attention of researchers due to its potential applications in e-learning, health care, marketing, etc. There is a need for standard benchmark corpora to develop and evaluate multi-label emotion classification methods. The majority of benchmark corpora were developed for the English language (monolingual corpora) using tweets. However, the multi-label emotion classification problem is not explored for code-mixed text, for example, English and Roman Urdu, although the code-mixed text is widely used in Facebook posts/comments, tweets, SMS messages, particularly by the South Asian community. For filling this gap, this study presents a large benchmark corpus for the multi-label emotion classification task, which comprises 11,914 code-mixed (English and Roman Urdu) SMS messages. Each code-mixed (English and Roman Urdu) SMS message manually annotated using a set of 12 emotions, including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust, and neutral (no emotion). As a secondary contribution, we applied and compared state-of-the-art classical machine learning (content-based methods – three word n-gram features and eight character n-gram features), deep learning (CNN, RNN, Bi-RNN, GRU, Bi-GRU, LSTM, and Bi-LSTM), and transfer learning-based methods (BERT and XLNet) on our proposed corpus. After our extensive experimentation, the best results were obtained using state-of-the-art classical machine learning methods on word uni-gram (Micro Precision = 0.67, Micro Recall = 0.54, Micro F ₁ = 0.67) with a combination of OVR multi-label and SVC single-label machine learning algorithms. Our proposed corpus is free and publicly available for research purposes to foster research in an under-resourced language (Roman Urdu).

Automatic detection of threatening language is an important task, however, most of the existing studies focused on English as the target language, with limited work on low-resource languages. In this paper, we introduce and release a new dataset for threatening language detection in Urdu tweets to further research in this language. The proposed dataset contains 3,564 tweets manually annotated by human experts as either threatening or non-threatening. The threatening tweets are further classified by the target into one of two types: threatening to an individual person or threatening to a group. This research follows a two-step approach: (i) classify a given tweet as threatening or non-threatening and (ii) classify whether a threatening tweet is used to threaten an individual or a group. We compare three forms of text representation: two count-based, where the text is represented using either character $n$ -gram counts or word $n$ -gram counts as feature vectors and the third text representation is based on fastText pre-trained word embeddings for Urdu. We perform several experiments using machine learning and deep learning classifiers and our study shows that an MLP classifier with the combination of word $n$ -gram features outperformed other classifiers in detecting threatening tweets. Further, an SVM classifier using fastText pre-trained word embedding obtained the best results for the target identification task.

Despite the many proposals to solve the neural machine translation (NMT) problem of low-resource languages, it continues to be difficult. The issue becomes even more complicated when few resources cover only a single domain. In this paper, we discuss the applicability of a source-side monolingual dataset of low-resource languages to improve the NMT system for such languages. In our experiments, we used Wolaytta–English translation as a low-resource language. We discuss the use of self-learning and fine-tuning approaches to improve the NMT system for Wolaytta–English translation using both authentic and synthetic datasets. The self-learning approach showed +2.7 and +2.4 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively, over the best-performing baseline model. Further fine-tuning the best-performing self-learning model showed +1.2 and +0.6 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively. We reflect on our contributions and plan for the future of this difficult field of study.

Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users’ emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).