sentimental_analysis

This repository contains a sentiment analysis project built using machine learning techniques. It includes pre-trained models, datasets, and vectorizers to analyze and classify text sentiment as positive or negative.

Project Structure

• glove/: Contains pre-trained GloVe embeddings with different dimensionalities (50d, 100d, 200d, 300d).
• kaggle/: Stores Kaggle dataset files or configurations.
• sentiment140.xlsx: Dataset used for training sentiment analysis models.
• logistic_model_with_vectorizer.sav: Combined logistic regression model and vectorizer.
• trained_logistic_model.sav: Standalone trained logistic regression model.
• trained_logistic_model_vectorizer.sav: Vectorizer for transforming text for the logistic model.
• trained_model.sav: Final trained model for prediction.
• vectorizer.sav: Saved vectorizer for text processing.
• modelLRBNB: Notebook with Logistic Regression and Naive Bayes training logic.
• model_predictions: Notebook for prediction and testing.
• glove.6B.50d-300d.txt: Pre-trained word embeddings.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/ManikPandey/sentimental_analysis_on_twitter_using_BNB_LR_LSTM.git