## 2 Related Work  
The rapid growth in the number of social media users, coupled with notable advances in natural language processing (NLP) and text mining technologies, has opened up new opportunities for mental health analysis [23; 24]. Social media platforms serve as valuable sources of large-scale and real-time data, offering insight into individuals' mental well-being, emotional states, and experiences [25]. Researchers have increasingly turned their attention to the wealth of textual data shared on these platforms to automatically detect signs of mental health issues such as depression, anxiety, and stress [26; 27]. These automated approaches have the potential to complement traditional methods of mental health evaluation, providing timely and cost-effective ways to identify people at risk and offer the necessary support and interventions. This has ignited significant research interest in exploring novel and innovative approaches to analyze textual data for signs of depression [28]. The detection of depression from textual data can be framed as a text classification problem, aiming to differentiate texts based on the presence or absence of signs of depression. Depression detection methods have predominantly approached the task as a binary classification problem, that is, determining whether users are depressed or not [29]. However, the nature of depression is diverse, with varying severity levels that encompass minimal, mild, moderate, and severe cases [7]. This has increased the research on novel data collection and approaches that consider the multifaceted nature of depression [30; 31]. In recent years, the number of works that aim to capture the varying degrees of intensity in depression is increasing consistently [20; 32; 33; 34]. Regarding the techniques used, early approaches relied on lexicon-based feature extraction to capture specific linguistic patterns associated with depressive language [35]. These features are extracted from posts and then used to train traditional machine learning algorithms [36; 37]. Although these methods provide valuable insights, they face challenges related to lexicon coverage, language nuances, and generalizability. The advent of word embeddings brought significant improvements to address these challenges. Word embeddings, such as Word2 Vec [38], Global Vectors for Word Representation (GloVe) [39] and FastText [40] represent words as dense and continuous vectors in a high-dimensional space that capture semantic and syntactic relationships. Researchers have leveraged these representations to develop more robust and context-aware models to identify depression [41]. These techniques provided a more nuanced and context-aware representation of words,enhancing the ability to identify subtle variations in language associated with depression. However, they do not take advantage of the individual affective or social value of words. Combining word embeddings with lexicon-based features helps to address this limitation, producing more robust and comprehensive representations of textual data [42]. This fusion benefits from domain-specific insights and interpretability from lexicon-based features while retaining the underlying semantic and contextual information provided by word embeddings. Moreover, traditional machine-learning approaches have gradually been surpassed by more sophisticated techniques. Deep neural networks such as the recurrent neural network (RNN) [43], long-short-term memory (LSTM) [44; 45], and bidirectional gated recurrent unit (BGRU) [46] have shown notable performance in the detection of depression. More recently, the field has witnessed a significant shift towards transformer-based models, especially with the introduction of models such as bidirectional encoder representations from transformers (BERT) [47]. These models can contextually encode the entire post, capturing the interaction of words and their meanings, resulting in more accurate and nuanced predictions in various Natural Language Processing (NLP) tasks, including depression detection [33].