147 lines
3.5 KiB
Python
147 lines
3.5 KiB
Python
import marimo
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__generated_with = "0.18.3"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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import pandas as pd
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from pathlib import Path
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from utils import create_sentiment_matrix
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INPUT_DIR = Path("./data/processing/02_taguette_postprocess")
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WORKING_DIR = Path('./data/processing/03_sentiment_analysis')
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if not WORKING_DIR.exists():
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WORKING_DIR.mkdir(parents=True)
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return INPUT_DIR, Path, WORKING_DIR, create_sentiment_matrix, mo, pd
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Load Sentiment CSV
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""")
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return
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@app.cell
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def _(INPUT_DIR, mo):
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csv_files = list(INPUT_DIR.glob("*.csv"))
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file_options = {f.stem: str(f) for f in csv_files}
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sentiment_csv = mo.ui.dropdown(
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options=file_options,
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label="Select Sentiment CSV File",
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full_width=True
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)
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sentiment_csv
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return (sentiment_csv,)
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@app.cell
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def _(Path, pd, sentiment_csv):
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input_csv_name = Path(sentiment_csv.value).stem
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timestamp = input_csv_name.split('_')[-1]
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doc = input_csv_name.split('_')[0]
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sentiment_df = pd.read_csv(sentiment_csv.value)
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sentiment_df
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return doc, sentiment_df, timestamp
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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# Phase 1: Individual interview analysis
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- Create sentiment matrices for each interview (document)
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- Save the intermediate results to file in the `WORKING_DIR`
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 1.1: Voice Sample vs. Theme Sentiment Matrix
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For each interview (document), create a matrix where:
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- Rows represent the different Voices (based on '_V-' tags)
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- Columns represent the different VoiceThemes(based on 'VT -' tags)
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- Each cell contains the aggregated sentiment score (sum) for that Voice/Theme combination
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""")
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return
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@app.cell
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def _(create_sentiment_matrix, sentiment_df):
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voice_matrix = create_sentiment_matrix(sentiment_df, column_prefix='VT - ', row_prefix='_V-')
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voice_matrix
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return (voice_matrix,)
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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SAVE TO CSV
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""")
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return
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@app.cell
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def _(WORKING_DIR, doc, timestamp, voice_matrix):
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# Save to CSV
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voice_filename = WORKING_DIR / f"{doc}_voice_theme_matrix_{timestamp}.csv"
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voice_matrix.to_csv(voice_filename)
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print(f"Saved to '{voice_filename}'")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 1.2: Character Sample vs. Theme Sentiment Matrix
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For each interview (document), create a matrix where:
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- Rows represent the different Characters (based on '_C-' tags)
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- Columns represent the different CharacterThemes (based on 'CT -' tags)
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- Each cell contains the aggregated sentiment score (sum) for that Character/Theme combination
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""")
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return
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@app.cell
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def _(create_sentiment_matrix, sentiment_df):
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character_matrix = create_sentiment_matrix(sentiment_df, column_prefix='CT - ', row_prefix='_C-')
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character_matrix
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return (character_matrix,)
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@app.cell
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def _(WORKING_DIR, character_matrix, doc, timestamp):
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# Save to CSV
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character_filename = WORKING_DIR / f"{doc}_character_theme_matrix_{timestamp}.csv"
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character_matrix.to_csv(character_filename)
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print(f"Saved to '{character_filename}'")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md(r"""
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## Step 1.3: Chase Brand Sentiment
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TODO: not sure we have enough supporting data for this yet
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""")
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return
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if __name__ == "__main__":
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app.run()
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