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This article mainly introduces "what are the features of wordmesh". In daily operation, I believe many people have doubts about the characteristics of wordmesh. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful for you to answer the doubts about "what are the features of wordmesh?" Next, please follow the editor to study!

Word cloud map is equivalent to the first step of data analysis-descriptive statistics. It makes it more convenient for us to extract keywords from the text and better grasp the overall information of the text.

Wordmesh uses language models pre-loaded in spacy packages to build text features, graph-based algorithms to extract keywords, multidimensional scaling to place these keywords on canvas, and so on.

Wordmesh features:

Keyword extraction: in addition to word frequency-based extraction, wordmesh also supports extraction methods based on textrank, sgrank and bestcoverage.

Word clustering: words can be drawn on the canvas based on semantic similarity, collinearity, and other attributes

Keyword filtering: extracted keywords can be filtered based on the word pos-tag or named entity

Font color and font size: font size and color can be set based on word frequency, pos-tag (emotional polarity), etc.

Examples

This is the visualization of the force-oriented (force-directed) algorithm. These words are extracted using textrank from the "international law" textbook and combined on the canvas according to their frequency of co-occurrence. The color represents the pos tag of the word.

This is Steve Jobs' commencement speech at Stanford. Keywords are extracted by textrank and clustered according to their textrank scores. Font color and font size are also functions of textrank scores.

This comes from the same text, but here it is clustered based on the frequency of keyword co-occurrence. The same clustering words use the same criteria to specify colors. You can see this from the position of the words. You can see that words like 'hungry'' and 'foolish'' are grouped together because they are close to each other in the text as part of the famous quotation "Stay hungry. Stay foolish".

This is all the adjectives used in the 2016 US presidential debate between Trump and Hillary Clinton. Words are clustered according to their meaning, the font size indicates the frequency of use, and the color corresponds to the candidate who uses them.

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