nlplot 1.1.0

Visualization Module for Natural Language Processing

nlplot

Visualization Module for Natural Language Processing

Description

Facilitates the visualization of natural language processing and provides quicker analysis

You can draw the following graph

1. N-gram bar chart
2. N-gram tree Map
3. Histogram of the word count
4. wordcloud
5. co-occurrence networks
6. sunburst chart
7. pyLDAvis

（Tested in English and Japanese）

Requirement

• python package

Install

pip install nlplot

Usage

sample df

df.head()

	text
0	Think rich look poor
1	When you come to a roadblock, take a detour
2	When it is dark enough, you can see the stars
3	Never let your memories be greater than your dreams
4	Victory is sweetest when you’ve known defeat

import nlplot

# taget_col as a list type or a string separated by a space.
npt = nlplot.NLPlot(df, taget_col='text')

# 1. N-gram bar chart
npt.bar_ngram(title='uni-gram', ngram=1, top_n=50)
npt.bar_ngram(title='bi-gram', ngram=2, top_n=50)

# 2. N-gram tree Map
npt.treemap(title='Tree of Most Common Words', ngram=1,top_n=30)

# 3. Histogram of the word count
npt.word_distribution(title='number of words distribution')

# 4. wordcloud
npt.wordcloud()

# 5. co-occurrence networks
npt.build_graph(min_edge_frequency=10)
# The number of nodes and edges to which this output is plotted.
# If this number is too large, plotting will take a long time, so adjust the [min_edge_frequency] well.
>> node_size:70, edge_size:166
npt.co_network(title='Co-occurrence network')

# 6. sunburst chart
npt.sunburst(title='sunburst chart', colorscale=True)

# 7. pyLDAvis
npt.ldavis(num_topics=5, passes=5, save=False)

Document

TBD

Test

TBD

Other

• Plotly is used to plot the figure

  • https://plotly.com/python/

• co-occurrence networks is used to calculate the co-occurrence network

  • https://networkx.github.io/documentation/stable/tutorial.html

• The following is used to plot pyLDAvis

  • https://github.com/bmabey/pyLDAvis

• wordcloud uses the following fonts

  • https://mplus-fonts.osdn.jp/about.html