A Study of Deep Learning-Based Text Representation and Classification Methods

Abstract

The advent of the information age, coupled with the extensive implementation of large-scale informatization initiatives, has triggered an explosive growth of digital text data. This deluge of information presents a paramount challenge: how to efficiently and accurately extract actionable insights and effective knowledge from complex, high-dimensional text corpora. The core of this endeavor lies in the fundamental tasks of textual analysis and categorization. This paper provides a comprehensive elaboration on the persistent problems and corresponding innovative solutions within the critical pipeline of text classification, which is fundamentally underpinned by text representation. Conventional text representation methods, such as Bag-of-Words (BoW) and TF-IDF, while intuitive, often grapple with the "curse of dimensionality," data sparsity, and an inability to capture semantic and syntactic nuances, leading to suboptimal feature selection and diminished representational efficacy. The selection of discriminative and non-redundant text features thus remains a significant challenge. In recent years, the methodological landscape for text representation and classification has diversified considerably, introducing techniques ranging from traditional machine learning models (e.g., SVM, Naive Bayes) to more contemporary deep learning architectures. While these advancements have spurred innovation, they concurrently introduce new challenges, including sensitivity to imbalanced label distributions, which can bias models towards majority classes, and poor generalizability across different domains or datasets. To address these limitations, this paper introduces a novel perspective grounded in the deep learning domain. We systematically explore and evaluate advanced neural architectures—including Convolutional Neural Networks (CNNs) for local feature extraction, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential context modeling, and Transformer-based models (e.g., BERT) for leveraging contextualized word embeddings. The primary objective is to propose and validate a framework that enhances the robustness of text representation, mitigates the impact of label imbalance through advanced sampling or loss functions, and ultimately improves classification accuracy and generalization capability. By leveraging the hierarchical feature learning and representation power of deep models, this research aims to continuously optimize the acquisition of text information and significantly improve the efficiency and precision of knowledge discovery in the era of big data.