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To generate the point word-clouds, the texts are analyzed using the natural language technique word2vec. The resutling vector space is of very high dimensionality, thus cannot be easily visualized. To reduce the high dimensional space to three.dimensions the method t-distributed stochastic neighbor embedding is used, which keeps words close together that were close in the high dimensional space. | |||
[[File:VRI-LEK-Epochs.PNG|400px]] | [[File:VRI-LEK-Epochs.PNG|400px]] | ||
[[File:VRI-LEK-Graph.png|400px]] | [[File:VRI-LEK-Graph.png|400px]] | ||
Revision as of 11:54, 2 November 2020
Title
Context:
Concept:
Approach:
To generate the point word-clouds, the texts are analyzed using the natural language technique word2vec. The resutling vector space is of very high dimensionality, thus cannot be easily visualized. To reduce the high dimensional space to three.dimensions the method t-distributed stochastic neighbor embedding is used, which keeps words close together that were close in the high dimensional space.
