Project Background
Topic modeling is a statistical technique aimed at uncovering latent semantic structures within a collection of documents, enabling the discovery of underlying themes or topics. With the exponential growth of digital content, such as articles, social media posts, and academic papers, there is an increasing need for automated methods to organize, categorize, and extract meaningful insights from these vast amounts of textual data. Topic modeling is a powerful tool in natural language processing (NLP) and information retrieval, facilitating tasks such as document clustering, summarization, and recommendation systems. Traditional topic modeling algorithms like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) rely on probabilistic and linear algebraic methods to infer topics based on word co-occurrence patterns. However, the emergence of deep learning techniques in neural network-based models has led to significant advancements in topic modeling.
Deep learning models such as neural topic models and hierarchical attention networks can capture complex semantic relationships and dependencies in text data, resulting in more accurate and interpretable topic representations. Moreover, deep learning-based topic modeling approaches offer the flexibility to handle various types of textual data, including short texts, multilingual documents, and noisy user-generated content. Thus, the topic modeling reflects the growing demand for efficient and scalable methods to uncover hidden structures and extract actionable insights from large-scale text corpora in diverse domains such as social media analytics, information retrieval, and content recommendation systems.
Problem Statement
- Extracting meaningful topics from unstructured text data is challenging due to the inherent ambiguity and complexity of natural language.
- Traditional topic modeling techniques struggle to efficiently handle the high-dimensional feature space of text data, leading to computational inefficiency and scalability issues.
- Ensuring generated topics are interpretable and coherent to users remains a significant challenge in applications where human understanding is crucial.
- Integrating multiple modalities, such as text, images, and metadata, into topic modeling frameworks poses a challenge for capturing diverse and rich semantic representations.
Aim and Objectives
- Develop efficient methods for extracting meaningful topics from unstructured text data.
- Enhance the accuracy and robustness of topic modeling algorithms to extract latent themes from text corpora.
- Improve the scalability and computational efficiency of topic modeling techniques for large-scale text datasets.
- Enhance the interpretability of generated topics to facilitate human understanding and decision-making.
- Explore multimodal topic modeling approaches to capture rich semantic representations from diverse data sources.
- Validate the performance of topic modeling methods through rigorous evaluation on benchmark datasets and real-world applications.
Contributions to Topic Modeling
- Facilitates the extraction of latent themes and patterns from unstructured text data, enhancing understanding and insights.
- Efficiently organize large volumes of textual data into coherent and interpretable topics, aiding information retrieval and organization.
- Advances in topic modeling techniques improve scalability, enabling the analysis of massive text corpora with minimal computational resources.
- Approaches that integrate multiple data modalities enable the extraction of richer semantic representations from diverse sources, contributing to more comprehensive analysis and understanding.
Deep Learning Algorithms for Topic Modeling
- Latent Dirichlet Allocation (LDA) with Neural Variational Inference
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Neural Topic Models (NTMs)
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Hierarchical Attention Networks (HANs)
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Recurrent Neural Networks (RNNs) with Attention Mechanisms
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Variational Autoencoders (VAEs) for Topic Modeling
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Generative Adversarial Networks (GANs) for Topic Modeling
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Transformer-based Models for Topic Modeling
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Graph Neural Networks (GNNs) for Topic Modeling
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Deep Boltzmann Machines (DBMs) for Topic Modeling
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Capsule Networks for Topic Modeling
Datasets for Topic Modeling
- Reuters-21578
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Associated Press (AP) News Corpus
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NIPS (Neural Information Processing Systems) Papers
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ArXiv Academic Papers
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PubMed Articles
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Stack Overflow Questions and Answers
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Wikipedia Articles
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Twitter Tweets
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Reddit Posts
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
