quick-sentiments

💬 Sentiment Analysis Pipeline

This repository hosts an easy-to-use, ready-made Sentiment Analysis pipeline designed to get you started quickly with classifying text data. Everything you need, from data preprocessing to model training and prediction, is set up and configured.

✨ Features

• End-to-End Pipeline: Go from raw text to sentiment predictions with minimal setup.
• Automated Preprocessing: Includes robust text cleaning:
  • Lemmatization
  • Stop word removal
  • Punctuation handling
  • URL/emoji/HTML removal, etc.
• Multiple Text Representation Methods:
  • Bag-of-Words (BoW)
  • Term Frequency (TF)
  • TF-IDF (Term Frequency-Inverse Document Frequency)
  • Word Embeddings (Word2Vec - pre-trained Google News 300-dim model)
• Multiple Machine Learning Algorithms:
  • Logistic Regression
  • Random Forest
  • XGBoost
• Hyperparameter Tuning Support:
  • All models are compatible with GridSearchCV.
  • By default, models run with standard parameters for quick testing.
  • Grid search options are built-in and ready to use if needed.
• Modular Design: Each component is cleanly separated into its own module.
• Prediction on New Data: Easily apply your trained model to new, unseen data.

🚀 Getting Started

Follow these steps to get your sentiment analysis pipeline up and running:

1. Prerequisites

• Git: For cloning the repository.
• Python 3.8+ (Recommended: Anaconda for environment management)
• Anaconda/Miniconda: Strongly recommended

2. Clone the Repository

git clone https://github.com/AlabhyaMe/Sentimental-Analysis-.git
cd Sentimental-Analysis-
conda create -n sentiment_env python=3.9
conda activate sentiment_env
pip install -r requirements.txt

This project is setup in the follwing pipeline
├── Training Data/
│   └── train.csv                # Your training file
├── New Data/
│   └── new_texts.csv            # Your new prediction file
├── MLAlgo/
│   ├── logistic_regression_model.py
│   ├── random_forest_model.py
│   └── xgboost_model.py
├── Vect/
│   ├── bag_of_words_vectorizer.py
│   ├── tfidf_vectorizer.py
│   └── word_embedding_vectorizer.py
├── preprocessing.py             # Text cleaning functions
├── sentiment_analysis_main.ipynb  # Full training + prediction notebook
├── sentiment_prediction.ipynb     # Quick prediction-only notebook
├── requirements.txt
└── README.md

3. Prepare Your Data

📌 Training Data

Place your training CSV file in the Training Data/ folder.

• It must contain:
  • A column named Response – for the raw input text.
  • A column named Sentiment – for sentiment labels (e.g., "Positive", "Negative", "Neutral").

📌 New Data for Prediction

Place your new prediction CSV file in the New Data/ folder.

• It must contain:
  • A column named RawTextColumn (or another name you configure in the notebook).

📚 Dataset Citation

This project uses publicly available training data from:

Madhav Kumar Choudhary. Sentiment Prediction on Movie Reviews. Kaggle.
https://www.kaggle.com/datasets/madhavkumarchoudhary/sentiment-prediction-on-movie-reviews
Accessed on: 2025- 07-15

If you use this dataset in your own work, please cite the original creator as per Kaggle's Terms of Use.