How Textvisualization.app and its semascope 👁️ compare with traditional tag clouds?
In this Post
Let's compare semascope 👁️ text visualizer with a traditional tag cloud, highlighting the distinctive features of Textvisualization.app:Table of Content
Intro
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Semascope 👁️ is a Javascript text visualizer that distinguishes itself from traditional tag clouds by incorporating multiple dimensions, providing a highly interactive user experience, and offering advanced features for entity recognition. These attributes make it a powerful tool for in-depth text analysis and visualization.
More importantly, semascope relies on a new method of keyword extraction, called Negative Binomial Distribution method, that you can use to extract keywords and tag your collection of texts. In fact you can use this keyword extraction method in a traditional tag cloud presentation, without the necessity to add semascope 👁️, our JS viewer, to your website.
What we have is two related products:
PythonandRscipts to mine collections of texts for keywords, based on a linguistically motivated statistical method. The scipts are called corpus_utils.Plotly Javascriptviewer app to interactively view word properties that were mined with the help ofcorpus_utils. This JS viewer is called semascope 👁️.
This website is a demonstration of how these components, keyword extraction and visualization, can work together.
Metrics Used
Tag Cloud:
- Typically relies on a single metric, word frequency. It represents words based on their occurrence in the text, with more frequent words appearing larger.
- Typically relies on a single metric, word frequency. It represents words based on their occurrence in the text, with more frequent words appearing larger.
semascope 👁️ text visualizer:
- Utilizes four dimensions - word frequency, document frequency, and a pair of estimated parameters of document frequency distributions, k and p. This gives a richer understanding of the significance of words within the context of the entire document collection.
Interactivity
Tag Cloud:
- Traditionally static and lacks interactive features. Users typically view a fixed representation of words based on predefined criteria.
semascope 👁️ text visualizer:
- Offers a highly interactive experience. Users can view, filter, zoom in and out, and explore word clusters dynamically. This interactivity allows for a more nuanced exploration of the data, enabling users to uncover insights effortlessly.
Entity Recognition
Tag Cloud:
- Primarily focuses on individual words without providing insights into entities, themes, or relationships within the text.
semascope 👁️ text visualizer:
- Goes beyond individual words and helps identify main actors and other important entities in a large collection of texts. This feature enhances the tool’s capability for advanced narrative analysis.
Dimensionality
Tag Cloud:
- Usually represents words in a two-dimensional space based on word frequency and font size.
semascope 👁️ text visualizer:
- Utilizes four dimensions, offering a more comprehensive representation of the importance and relevance of words. This multidimensional approach contributes to a more accurate and nuanced visualization.
Usability for Large Text Collections
Tag Cloud:
- May become cluttered and less informative when dealing with a large collection of texts.
semascope 👁️ text visualizer:
- Designed to handle large collections effectively, providing users with the ability to zoom in and out for a detailed or holistic view, making it suitable for comprehensive text analysis.
