Text preprocessing, representation and visualization from zero to hero.
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Updated
Aug 29, 2023 - Python
Text preprocessing, representation and visualization from zero to hero.
Beautiful visualizations of how language differs among document types.
Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)
Notebooks for the Seattle PyData 2017 talk on Scattertext
Multiplex: visualizations that tell stories—A Python library to create and annotate beautiful network graph visualizations, text visualizations and more.
A lightweight Python library for constructing, processing, and visualizing constituent trees.
Interactive visual tool for the demonstration of topic evolution
Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)
Visual analytics application for qualitative text analysis
A docsify plugin to render graph like plantuml, mermaid, ..etc
Visualización de discursos presidenciales Chilenos
This package integrates many basic Chinese NLP functions, making Python-based Chinese word processing and information extraction simple and convenient.
Coconut Libtool is the all-in-one data mining and textual analysis tool for librarians or anyone interested in these applications. Our tool does not require any prior knowledge of coding or programming, making it approachable and great for users who want to test out these data analysis and visualization techniques.
integrates spatial and textual data processing tools into a modular software package which features preprocessing, geocoding, disambiguation and visualization
Dataiku DSS plugin to visualize text as word clouds in 59 languages ☁
Data Visualization Projects
Visualization of the most frequent words in the German 2019 EU election programs
Vehicle complaints visualization: Exploring entities with non-photorealistic rendering.
This repository consists of Bengali Text-Visualization using Word2Vec Model. A mini project under the mentorship of Prof. Sandipan Ganguly, HIT-K.
Empirical applications on Amerd conflict event using ACLED data
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