Sentiment-Analysis-Model

This project is a Sentiment Analysis Model developed using Python and various data science libraries like matplotlib, scikit-learn, seaborn on the Google Colab platform. The model is designed to classify text data into positive, negative, or neutral sentiments with an accuracy of 85%.

Key Features:

Platform: Google Colab Programming Language: Python Libraries Used: 1. Pandas: For data manipulation and analysis.
2. NumPy: For numerical computations.
3. Scikit-Learn: For building the machine learning model, including data preprocessing, vectorization, and model evaluation.
4. Matplotlib & Seaborn : For visualizing the data and model performance.

Model Overview:
**Data Source: The dataset used for training the model was processed and cleaned using Pandas, ensuring the data was in a suitable format for analysis.
**Feature Extraction: Text data was converted into numerical form using techniques like TF-IDF, CountVectorizer.
**Model: Various machine learning algorithms were tested, with the final model being chosen based on performance metrics. The model was trained and evaluated on the dataset, achieving an accuracy of 85%.
**Evaluation: The model's performance was evaluated using standard metrics like accuracy, precision, recall, and F1-score. Cross-validation was also employed to ensure robustness

How to Run the Model:
1. Clone the Repository:
git clone https://github.com/usha-madithati/Sentiment-Analysis-Model
cd sentiment-analysis-model
2. Dependencies : Install the required python packages: pip install -r requirements.txt( install all the required packages like pandas, numpy, scikit-learn, matplotlib, seaborn.
3. Run the model : Execute the google colab or Jupyter Notebook to see the model in action Upload the "Sentiment-Analysis.ipynb" notebook to colab, install all the required dependencies and run all cells to execuete the model.

Results:
1. The model achieved an accuracy of 85% on the test dataset.
2. Detailed performance metrics and confusion matrix are provided in the notebook.

Future Enhancements:
1. Improve model accuracy by experimenting with advanced techniques like word embeddings (Word2Vec, GloVe) and deep learning models.
2. Expand the dataset to include more diverse text samples for better generalization.

Conclusion: This Sentiment Analysis Model serves as a foundational implementation for understanding the basics of text classification using machine learning. It can be further enhanced and deployed in real-world applications requiring sentiment detection.