PLATO

Philosophical Language Analysis for Topic and Opinion Mining

PLATO is a research project focused on applying Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze philosophical texts, with a primary focus on Plato’s Republic, as well as works from Aristotle, Kant, and other classical philosophers.

Task 1: Topic Modeling on Plato’s Republic

Apply classical topic modeling techniques and modern transformer-based methods to analyze Plato’s Republic dataset. The objective is to compare the performance of traditional and deep learning-based topic modeling approaches in capturing themes and arguments in philosophical texts.

Methods to Be Used

  1. Classical Topic Models
    • Latent Dirichlet Allocation (LDA) – Probabilistic model that assigns words to topics based on word co-occurrence patterns.
    • Latent Semantic Analysis (LSA) – Singular value decomposition (SVD)-based method that captures latent structures in text.
    • Non-Negative Matrix Factorization (NMF) – Decomposes word-document matrices into non-negative components to extract topics.
  2. Transformer-Based Models
    • BERTopic – Uses transformer embeddings and clustering to generate coherent topics.
    • Top2Vec – Learns topic representations by jointly embedding documents and words in a continuous space.

Goals of Task 1

  • Extract dominant themes from Plato’s Republic using different topic modeling methods.
  • Compare topic coherence scores across classical and transformer-based models.
  • Analyze thematic differences between classical and modern NLP techniques.
  • Visualize topic distributions and relationships between themes.

This task will serve as the foundation for subsequent argument mining and complexity modeling in the PLATO project.

References