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README.md

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----
-title: VADER Sentiment Analysis
-emoji: 👀
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 5.30.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+metadata
+title: VADER Sentiment Analysis
+emoji: 👀
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 5.30.0
+app_file: app.py
+pinned: false
+
+# VADER Sentiment Analyzer - Hugging Face Space
+
+This is a simple web app built with [Gradio](https://gradio.app) for performing sentiment analysis using VADER.
+
+## Features
+
+- Upload a CSV file.
+- Select the column that contains text data.
+- Automatically clean the text and compute VADER sentiment scores.
+- Assign sentiment labels: Positive, Neutral, or Negative.
+- Download the processed CSV file.
+
+## How to Use
+
+1. Upload your CSV file (e.g., `reviews.csv`).
+2. Click "Load Columns" to populate the dropdown with available columns.
+3. Select the column that contains the text (e.g., `ReviewBody`).
+4. Click "Run Sentiment Analysis".
+5. Download the processed file with the sentiment labels.
+
+## Output File
+
+The processed file will be saved with the filename:
+

app.py

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+import pandas as pd
+import re
+import nltk
+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+nltk.download("vader_lexicon")
+sia = SentimentIntensityAnalyzer()
+
+def clean_text(text):
+    if not isinstance(text, str):
+        return ""
+    text = re.sub(r"http\S+", "", text)
+    text = re.sub(r"@\w+|#\w+", "", text)
+    text = re.sub(r"[^\w\s]", "", text)
+    text = text.lower().strip()
+    return text
+
+def get_sentiment_label(score, pos_thresh, neg_thresh):
+    if score >= pos_thresh:
+        return "Positive"
+    elif score <= neg_thresh:
+        return "Negative"
+    else:
+        return "Neutral"
+
+def analyze_sentiment(file, text_column, pos_thresh, neg_thresh):
+    try:
+        df = pd.read_csv(file.name)
+    except Exception as e:
+        return f"Error reading CSV file: {e}", None, None, None
+
+    if text_column not in df.columns:
+        return "Error: Selected column not found in the uploaded file.", None, None, None
+
+    df["clean_text"] = df[text_column].apply(clean_text)
+    df["compound"] = df["clean_text"].apply(lambda x: sia.polarity_scores(x)["compound"])
+    df["sentiment"] = df["compound"].apply(lambda score: get_sentiment_label(score, pos_thresh, neg_thresh))
+
+    # Save CSV
+    output_file = "VADER_sentiment_results.csv"
+    df.to_csv(output_file, index=False)
+
+    # Plot 1: Sentiment distribution
+    plt.figure(figsize=(6, 4))
+    sns.countplot(data=df, x="sentiment", palette="Set2")
+    plt.title("Sentiment Distribution")
+    plt.tight_layout()
+    sentiment_fig = "sentiment_dist.png"
+    plt.savefig(sentiment_fig)
+    plt.close()
+
+    # Plot 2: Compound score histogram
+    plt.figure(figsize=(6, 4))
+    sns.histplot(df["compound"], bins=30, kde=True, color="purple")
+    plt.title("Compound Score Distribution")
+    plt.xlabel("Compound Score")
+    plt.tight_layout()
+    compound_fig = "compound_dist.png"
+    plt.savefig(compound_fig)
+    plt.close()
+
+    return f"Sentiment analysis complete. Processed {len(df)} rows.", output_file, sentiment_fig, compound_fig
+
+def get_columns(file):
+    try:
+        df = pd.read_csv(file.name, nrows=1)
+        return list(df.columns)
+    except Exception:
+        return []
+
+with gr.Blocks() as demo:
+    gr.Markdown("## VADER Sentiment Analyzer with Custom Thresholds and Visualizations")
+    gr.Markdown("Upload a CSV, choose a text column, adjust thresholds, and view/download results.")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload CSV", file_types=[".csv"])
+        col_dropdown = gr.Dropdown(label="Select Text Column", choices=[], interactive=True)
+
+    load_columns_btn = gr.Button("Load Columns")
+    load_columns_btn.click(fn=get_columns, inputs=file_input, outputs=col_dropdown)
+
+    with gr.Row():
+        pos_thresh_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.05, step=0.01, label="Positive Threshold")
+        neg_thresh_slider = gr.Slider(minimum=-1.0, maximum=0.0, value=-0.05, step=0.01, label="Negative Threshold")
+
+    analyze_btn = gr.Button("Run Sentiment Analysis")
+
+    output_text = gr.Textbox(label="Status")
+    file_output = gr.File(label="Download Processed CSV")
+    sentiment_plot = gr.Image(label="Sentiment Distribution")
+    compound_plot = gr.Image(label="Compound Score Distribution")
+
+    analyze_btn.click(
+        fn=analyze_sentiment,
+        inputs=[file_input, col_dropdown, pos_thresh_slider, neg_thresh_slider],
+        outputs=[output_text, file_output, sentiment_plot, compound_plot]
+    )
+
+demo.launch(share=True, debug=True)

requirements.txt

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+gradio
+pandas
+nltk
+matplotlib
+seaborn