Sentiment Prediction Project

Overview

This project focuses on predicting the sentiment (positive, negative, or neutral) of text data using machine learning techniques. Sentiment prediction has various applications, including customer feedback analysis, social media monitoring, and more.

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Features

• Preprocessing of text data (tokenization, stopword removal, stemming, etc.).
• Feature extraction using techniques like TF-IDF or word embeddings.
• Implementation of machine learning models such as Logistic Regression, Naïve Bayes, or deep learning models like LSTM.
• Evaluation metrics for assessing model performance, such as accuracy, precision, recall, and F1-score.
• A user-friendly interface (if applicable) for testing sentiment predictions.

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Technologies Used

• Programming Language: Python
• Libraries and Frameworks:
  • Pandas
  • NumPy
  • Scikit-learn
  • TensorFlow/Keras (optional for deep learning)
  • NLTK or spaCy (for text preprocessing)
  • Matplotlib/Seaborn (for data visualization)

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Project Structure

SentimentPrediction/
├── data/                # Dataset files
├── notebooks/           # Jupyter notebooks for exploration
├── src/                 # Source code for models and utilities
├── models/              # Saved trained models
├── results/             # Output and evaluation results
├── requirements.txt     # Dependencies
└── README.md            # Project documentation

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Dataset

The dataset used for this project consists of labeled text data (e.g., movie reviews, tweets). Each entry includes:

• Text: The textual content.
• Sentiment: The corresponding label (e.g., positive, negative, neutral).

If using a public dataset, provide the source (e.g., IMDB Dataset).

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Usage

1. Preprocess the dataset by running the preprocessing script in src/preprocessing.py.
2. Train the model using the training script in src/train_model.py.
3. Evaluate the model performance with src/evaluate_model.py.
4. (Optional) Test the model interactively or deploy it using the script in src/deploy.py.

For example:

python src/train_model.py --data_path data/dataset.csv --model_output models/sentiment_model.pkl

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Evaluation Metrics

The following metrics are used to evaluate model performance:

• Accuracy: Percentage of correct predictions.
• Precision: Proportion of positive identifications that were actually correct.
• Recall: Proportion of actual positives identified correctly.
• F1-score: Harmonic mean of precision and recall.

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Results

Include visualizations and summaries of model performance. Example:

• Confusion matrix
• ROC curve
• Accuracy, precision, recall, and F1-score values