Movie Review Sentiment Intelligence Dashboard

An end-to-end Natural Language Processing (NLP) machine learning application that analyzes sentiment from movie reviews. The system classifies textual reviews as Positive or Negative with 89.24% accuracy using a Logistic Regression model trained on 50,000 IMDB reviews.

It features a full-fledged Flask Web Application with a custom CSS dashboard supporting both Batch Document Analysis CSV and real-time Individual Review Testing.

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Features

High Accuracy ML Engine: Trained on 50,000 IMDB movie reviews using TF-IDF Vectorization with bigrams (ngram_range=(1,2)) and negation-preserving preprocessing. Multi-Format Batch File Processing: Upload datasets in CSV formats for bulk sentiment extraction. Visual Analytics: Automatically generates sentiment distribution metrics (Total Count, Positive %, Negative %) and a Matplotlib pie chart visualization. Single Review Tester: Instant prediction tool for testing individual sentences or custom reviews. Custom Modern Dashboard: Sleek, responsive tabbed UI built using pure CSS .

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Tech Stack & Libraries

Language: Python 3.13 Machine Learning & NLP: Scikit-Learn, NLTK, Pandas, Joblib Web Framework: Flask Data Visualization: Matplotlib Frontend: HTML5, Custom CSS, JavaScript

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Model Performance Comparison

During development, two models were trained and benchmarked on the test set (9,917 samples):

Model | Accuracy | F1-Score (Negative) | F1-Score (Positive) |

Logistic Regression (Selected) | 89.24% | 0.89 | 0.89 | Multinomial Naive Bayes | 86.82% | 0.87 | 0.87 |

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Project Structure

sentiment_flask_app/
│
├── sentiment_model.pkl       # Saved Logistic Regression Model
├── tfidf_vectorizer.pkl      # Saved TF-IDF Vectorizer
├── main.py                    # Main Flask Backend Logic
├── static/
│   └── style.css            # Custom Styling File
└── templates/
    └── index.html           # Dashboard Interface



Getting Started

1. Clone the Repository

git clone (https://github.com/abhisheknishad23/Sentiment-Analysis.git)
cd Sentiment-Analysis

2. Create and Activate Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install flask pandas scikit-learn joblib matplotlib nltk

4. Run the Flask Application

python app.py
Open your browser and navigate to http://127.0.0.1:5000/