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Analysis of Bipartite Networks in Anime Series Project Description This project explores how the descriptions of anime series influence their connections, using natural language processing (NLP) techniques and network analysis. The main objective is to identify and analyze patterns in the description that link different anime, through bigrams (...)

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Analysis of Bipartite Networks in Anime Series Project Description This project explores how the descriptions of anime series influence their connections, using natural language processing (NLP) techniques and network analysis. The main objective is to identify and analyze patterns in the descriptions that link different animes, through bigrams, topic clustering, and exponential random graph models (ERGM).

Project Content Data Collection: Gathering descriptions of anime series from a database. Natural Language Processing (NLP): Analyzing bigrams in the anime descriptions. Topic Clustering: Grouping animes based on the predominant themes in their descriptions. Exponential Random Graph Models (ERGM): Constructing and analyzing bipartite networks to study the connections between animes. Project Structure data/: Contains collected and processed data. scripts/: Scripts used for data processing and analysis. results/: Analysis results, including visualizations and graph models. Requirements Python 3.8 or higher Libraries: pandas, numpy, nltk, scikit-learn, networkx, matplotlib

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Este proyecto está licenciado bajo la Licencia MIT. Para más detalles, consulta el archivo LICENSE.

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Analysis of Bipartite Networks in Anime Series Project Description This project explores how the descriptions of anime series influence their connections, using natural language processing (NLP) techniques and network analysis. The main objective is to identify and analyze patterns in the description that link different anime, through bigrams (...)

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