stefrodopcodes94 / social-graphs-project-2021

Social Graphs

Social Graphs: Text Analysis of [Avatar]

Overview

This project, "Social Graphs", represents a comprehensive data analysis initiative focused on the extraction and examination of textual content from the popular TV series, [Avatar]. By employing advanced Natural Language Processing (NLP) techniques, we have transformed the intricate web of character interactions and dialogues into an informative graph structure. This approach not only elucidates the dynamic relationships between characters but also uncovers underlying themes and patterns within the narrative.

Key Features

• Graph-Based Analysis: Conversion of character interactions into a graph for in-depth relational analysis.
• NLP Techniques: Utilization of cutting-edge NLP methods to process and analyze the text, including:
  • Named Entity Recognition (NER) to identify and classify proper names.
  • Sentiment Analysis to gauge the emotional undertones of dialogues.
  • Topic Modeling to discover the latent topics within the text.
  • Co-occurrence Analysis to explore the strength and dynamics of character interactions.

Project Structure

• explainer.ipynb: A Jupyter notebook that serves as the comprehensive guide to our methodology, including code snippets, analysis, and insights. View the notebook.

Data Source

The textual content analyzed in this project was meticulously extracted from the scripts of Avatar. This dataset includes dialogues, character names, and descriptive actions, providing a rich source for our NLP-driven exploration.

Results and Implications

Our analysis has revealed significant insights into the social dynamics and thematic elements of [Avatar], demonstrating the power of combining graph theory with NLP. The graphical representation of character relationships, coupled with the depth of textual analysis, offers a novel perspective on narrative structures and character development.

This project is not only a testament to the versatility of NLP and graph theory in text analysis but also serves as a valuable tool for writers, sociologists, and fans alike to understand complex narrative ecosystems on a deeper level.

How to Use

To explore our findings and replicate the analysis, please refer to the explainer.ipynb notebook. It provides a detailed walkthrough of the data processing, analysis techniques, and visualization methods employed in this study.

Contribution

This project was conceived and developed by me and my team, with the goal of showcasing the intersection of NLP and graph theory in uncovering the nuances of narrative texts. We believe that our approach can be adapted and applied to various other textual datasets for similarly insightful analyses.