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Deep Learning–Driven Sentiment and Depression Analysis from Social Media Text: A Comprehensive Multilingual and Theoretical Investigation
Issue Vol. 2 No. 01 (2025): Volume 02 Issue 01 --- Section Articles
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Abstract
The exponential growth of social media platforms has transformed user-generated textual data into a critical resource for understanding public opinion, emotional expression, and psychological well-being. Among the most significant and socially impactful applications of sentiment analysis is the automated detection of depressive tendencies and emotional distress expressed through online discourse. Over the past decade, computational approaches to sentiment and mental health analysis have evolved from lexicon-based and classical machine learning paradigms to sophisticated deep learning architectures capable of modeling contextual, sequential, and semantic complexities in natural language. This article presents a comprehensive, publication-ready research investigation into deep learning–based sentiment and depression analysis from social media text, with particular emphasis on recurrent and hybrid neural architectures applied to multilingual and low-resource language contexts. Grounded strictly in the provided scholarly references, the study synthesizes theoretical foundations, historical developments, methodological considerations, and critical debates surrounding sentiment analysis and depression detection using social media data.
The work is theoretically anchored in prior studies on Twitter-based sentiment mining, distant supervision, and noisy text classification, while foregrounding neural network innovations such as convolutional neural networks, long short-term memory networks, bidirectional recurrent models, attention mechanisms, and hybrid CNN–LSTM architectures. Special analytical attention is given to depression analysis in the Bangla language using LSTM-based recurrent neural networks, as demonstrated in prior empirical research, which serves as a cornerstone for discussing the challenges and opportunities associated with mental health analytics in morphologically rich and underrepresented languages (Uddin et al., 2019). By situating this work within a broader ecosystem of sentiment analysis research spanning Arabic dialects, code-switched text, and multilingual social media corpora, the article critically examines how linguistic diversity, data sparsity, and sociocultural context influence model design, performance, and interpretability.
Methodologically, the paper elaborates a text-based experimental framework that integrates data collection, preprocessing, representation learning, model training, and evaluation, while also acknowledging inherent limitations such as annotation subjectivity, platform bias, and ethical concerns related to mental health inference. The results section provides an interpretive synthesis of findings reported across the referenced literature, emphasizing trends in model accuracy, robustness, and generalizability rather than numerical tabulation. The discussion advances a deep theoretical interrogation of competing scholarly viewpoints, addressing debates over explainability versus performance, the validity of social media as a proxy for mental health assessment, and the risks of algorithmic bias and overgeneralization.
By offering an extensive, critical, and integrative analysis, this article contributes a unified scholarly narrative that connects sentiment analysis and depression detection research across methodological traditions and linguistic settings. It underscores the necessity of context-aware, ethically grounded, and theoretically informed deep learning approaches for future advances in computational mental health and sentiment analytics.
Keywords
References
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