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Ranking categorization [38]. (a) represents hard categorization while (b) represents ranking categorization.
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The automated classification of texts into predefined categories has become increasingly prominent, driven by the exponential growth of digital documents and the demand for efficient organization. This paper serves as an in-depth survey of text classification and machine learning, consolidating diverse aspects of the field into a single, comprehens...
Citations
The identification of themes and motifs in literary texts is a fundamental aspect of literary analysis, traditionally performed through manual annotation and expert interpretation. However, the increasing availability of large-scale English literary corpora presents new challenges and opportunities for automated analysis. This paper proposes a deep learning (DL)-based framework for automatically detecting themes and motifs in extensive literary collections. The dataset comprises diverse sources, including classic literature, modern fiction, and poetry, ensuring a broad representation of thematic structures. A rigorous preprocessing pipeline is applied, involving stop word removal and tokenization to refine textual data. For feature extraction, Word2Vec is utilized to capture semantic relationships between words. The core novelty of this research lies in the implementation of a Duelist Algorithm-optimized Bi-directional Long Short-Term Memory (DAO-BiLSTM) model, which enhances the model’s ability to detect and classify recurring thematic elements with high accuracy. The proposed method achieves an accuracy of 96.24%, recall of 97.32%, precision of 95.6%, and an F1-score of 94.7%, demonstrating superior performance over existing methods. The model is implemented in Python 3.9 using TensorFlow in a high-performance computing environment, ensuring efficient processing of large-scale textual data. Experimental results illustrate the effectiveness of the proposed approach in identifying complex motifs and themes across various literary genres. These findings highlight the potential of DL in augmenting literary analysis, enabling large-scale, data-driven thematic exploration that complements traditional human-driven methodologies.
The rapid expansion of mobile mental health (mHealth) apps has redefined the landscape of digital mental health interventions, offering unprecedented accessibility and scalability while embedding users within algorithmic infrastructures of care and control. This study adopts a multidisciplinary framework, integrating Natural Language Processing (NLP) and move analysis to interrogate the linguistic, affective, and visual strategies shaping user engagement with mHealth apps. Through an extensive analysis of 30,000 user reviews from Headspace, Calm , and BetterHelp , the paper uncovers a structured rhetorical pattern in user feedback, revealing the interplay of empowerment, algorithmic governance, and neoliberal self-optimization. Key insights include four emerging inter-related repertoires across the dataset. These findings advance the concept of therapeutic surveillance advanced by the current research endeavor, illustrating how mHealth apps function as digital shepherds – nudging users toward self-regulation while extracting behavioral and emotional data.
