textagon 0.1.24

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.

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

Run DEMO

https://colab.research.google.com/drive/115P0Psl49CQIp9InJXJxQKMoN9NmfDvt

Installation

Initial Setup

pip install textagon 

Upgrading Textagon

pip install --upgrade textagon 

Additional Considerations

Installation

• Package versions needed (execution will stop via a check; will add requirements.txt in the future):
  • wn 0.0.23
• For SpaCy, run the following to get the 'en_core_web_sm' model ('en' in SpaCy 2.3.5):

python -m spacy download en_core_web_sm

• 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

Running Textagon

import pandas as pd
from textagon.textagon import Textagon
from textagon.AFRN import AFRN

### Test cases ###

df = pd.read_csv('../examples/dvd.txt', sep='\t', header=None, names=["classLabels", "corpus"])

tgon = Textagon(
    df, "dvd", 0, 0, 4, 3, "Lexicons_v5.zip", 
    1, 5, "bB", 0, 1, 0, 3, 1, 1, 1, 1, 1, "upload/exclusions.txt", "full",
    False
)

tgon.RunFeatureConstruction()
tgon.RunPostFeatureConstruction()