tommanzur/langchain_sentimen_analysis

The project showcases two main approaches: a baseline model using RandomForest for initial sentiment classification and an enhanced analysis leveraging LangChain to utilize Large Language Models (LLMs) for more in-depth sentiment analysi

Sentiment Analysis Project

Introduction

This GitHub repository hosts a comprehensive Jupyter Notebook focused on performing advanced sentiment analysis. The project showcases two main approaches: a baseline model using RandomForest for initial sentiment classification and an enhanced analysis leveraging LangChain to utilize Large Language Models (LLMs) for more in-depth sentiment analysis. This dual approach allows for a comparison of traditional machine learning techniques with cutting-edge NLP technologies.

Installation

To set up the project environment:

Clone the repository to your local machine:

git clone <repository-url>

Navigate to the repository directory, and install the required Python libraries:

pip install -r requirements.txt

Usage

To utilize the sentiment analysis notebook:

Open sentiment_Analysis.ipynb and execute the cells in order. The notebook is structured to guide you through the sentiment analysis process, starting with data preparation, followed by the RandomForest baseline model, and concluding with the advanced LLM analysis using LangChain.

Baseline Model with RandomForest

The notebook begins with a baseline sentiment analysis model using RandomForest. This section covers data preprocessing, feature extraction, and model training to establish a foundational performance benchmark.

Advanced Analysis with LangChain and LLMs

Subsequently, the notebook explores more sophisticated sentiment analysis techniques by integrating LangChain to leverage Large Language Models. This part demonstrates how to enhance sentiment analysis capabilities, offering insights into the application of LLMs in NLP tasks.