Textagon is a powerful tool for text data analysis, providing a means to visualize parallel representations of your data and gain insight into the impact of various lexicons on two classes of text data.
Project description
Textagon
Textagon is a powerful tool for text data analysis, providing a means to visualize parallel representations of your data and gain insight into the impact of various lexicons on two classes of text data.
- Parallel Representations
- Graph-based Feature Weighting
Installation
Prereqs
Installation
-
Package versions needed (execution will stop via a check; will add requirements.txt in the future):
- wn 0.0.23
-
For the spellchecker (which defaults to aspell):
- MacOS: brew install enchant
- Windows: pyenchant includes hunspell out of the box
- Linux: install libenchant via package manager
- For general notes, see: https://pyenchant.github.io/pyenchant/install.html
Initial Setup
pip install textagon
Upgrading Textagon
pip install --upgrade textagon
Running Textagon
import pandas as pd
from textagon.textagon import Textagon
from textagon.AFRN import AFRN
### Test cases ###
df = pd.read_csv(
'./sample_data/dvd.txt',
sep='\t',
header=None,
names=["classLabels", "corpus"]
)
tgon = Textagon(
inputFile=df,
outputFileName="dvd"
)
tgon.RunFeatureConstruction()
tgon.RunPostFeatureConstruction()
Project details
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