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

1. Generate representations

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

### 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()

2. Unzip stored representations

import zipfile
import os

# Specify the path to the zip file
zip_file_path = './output/distress_representations.zip'

# Specify the directory to extract files to
extract_to_directory = './output/distress_representations'

# Ensure the directory exists
os.makedirs(extract_to_directory, exist_ok=True)

# Open the zip file
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    # Extract all the contents
    zip_ref.extractall(extract_to_directory)

print(f"Files extracted to {extract_to_directory}")

3. Score and rank representations with tGBS.

featuresFile = './output/distress_key.txt'
trainFile = './output/distress.csv'
weightFile = './output/distress_weights.txt'


ranker=tGBS(
	featuresFile=featuresFile,
	trainFile=trainFile,
	weightFile=weightFile
)

ranker.RankRepresentations()