Electron microscopy
 
Word Cloud Visualization
- Python for Integrated Circuits -
- An Online Book -
Python for Integrated Circuits                                                                                   http://www.globalsino.com/ICs/        


Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

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Word Cloud is a visual representation of text data, which is helpful to get a high-level understanding about the important keywords from data in terms of its occurrence. In the visual display of a word cloud, the magnitude of each word represents its frequency or relevance, namely the size of a word is proportional to the word frequency. The Word Cloud is a data visualization tool for visualizing text data. In practice, a word cloud can be used to emphasise important textual data points. Data from social networking websites are frequently analyzed using word clouds. Therefore, presenting key phrases as word clouds is straight-forward. Using the word cloud representation gives an opportunity for an additional encoding of the importance of each key phrase, which is usually done by varying the font size in the word edits.

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code:     

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AAA: Word Cloud applications with self-defined stopwords in a text file. code:          
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings 
Input:                  
         Copied text from today's news.
Output:                  
         Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

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Simple Word Cloud applications with text input from a .txt file. code:          
          Find the best similarity with Word2Vec Models/word embeddings 
Input:                  
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

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Simple Word Cloud applications with text input from a .txt file (wtih "stopwords.update([])"). code:          
          Find the best similarity with Word2Vec Models/word embeddings 
Input:                  
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

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Simple Word Cloud applications with text input from a .csv file. code:          
          Find the best similarity with Word2Vec Models/word embeddings 
Input:                  
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
          Find the best similarity with Word2Vec Models/word embeddings
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

============================================

Simple Word Cloud applications. code:          
          Find the best similarity with Word2Vec Models/word embeddings 
Output:                  
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

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Word cloud applications. code:          
          Find the best similarity with Word2Vec Models/word embeddings 
          Find the best similarity with Word2Vec Models/word embeddings
Output:                  
          Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings
         Find the best similarity with Word2Vec Models/word embeddings

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AAA: Generates a word cloud visualization from a given text, utilizing a custom mask image for the shape of the word cloud. WordCloud in Python interprets pure white areas in the mask as areas where words can appear and black areas as areas where they cannot. Grey areas might be considered as intermediate opacity, resulting in partial visibility. code:

 

Input:

 

Output:

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