The Sentiment Analysis with NLP project is designed to classify sentiments from text data, specifically to determine whether a given text expresses a positive or negative sentiment. Using the Sentiment140 dataset, which contains a large number of tweets labeled for sentiment, this project involves data cleaning, text preprocessing, model training, and performance evaluation. The end result is a trained model capable of analyzing new text data to predict sentiment, which could be useful for applications in customer feedback analysis, social media monitoring, and brand sentiment tracking.
The project employs the Logistic Regression algorithm due to its efficiency and effectiveness in binary classification tasks like sentiment analysis. Additional steps include hyperparameter tuning to optimize model performance and saving the trained model and vectorizer, making the model ready for deployment and future predictions.
Technologies Used
- Python: Core programming language for implementing data processing, machine learning, and NLP tasks.
- Pandas: For data manipulation and handling of CSV files, enabling efficient loading, cleaning, and structuring of the dataset.
- NLTK (Natural Language Toolkit): Used for text preprocessing tasks such as tokenization and stopword removal, key steps in NLP.
- scikit-learn: Essential library for machine learning, used for model training, hyperparameter tuning (GridSearchCV), and evaluation metrics.
- Joblib: A library for saving trained machine learning models and vectorizers for later use, enabling efficient storage and reusability.
- Matplotlib and Seaborn: Visualization libraries used to create a confusion matrix and other plots for better insight into model performance.
This project demonstrates the full pipeline for a machine learning model: from data preprocessing to model evaluation, providing a practical example of how to implement a sentiment analysis system with Python and common data science libraries.