Using keyword extraction (PKE) and pre-trained word embeddings to create a semantic word cloud
from SemanticCloud.keywords import keywordExtractor
from SemanticCloud.wordembeddings import word2vec
from SemanticCloud.reduction import Mapper
from SemanticCloud.viz import createViz
import pandas as pd
import numpy as np
text = # Big old text document
# Get keywords, embeddings and map to 2D
keywords = keywordExtractor(text)
embeddings = word2vec(keywords)
data = Mapper(embeddings)
# Putting the data together and removing outliers
df = pd.merge(data, pd.DataFrame.from_dict(keywords, columns=['value'], orient='index'), left_index=True, right_index=True)
df = df[np.abs(df.X - df.X.mean()) <= (2 * df.X.std())]
df = df[np.abs(df.Y - df.Y.mean()) <= (2 * df.Y.std())]
# Create Visualisation
createViz(df,folder=folder)
Interactive Version (scroll out a little bit)
To authenticate vecshare you will need to run this command in your terminal:
dw configure and retrieve an API token from data.world