Twitter-Sentiment-Analysis

Project Overview

In this project, I developed a sentiment analysis model leveraging HuggingFace Transformers, including pipelines, AutoModel, and AutoTokenizer. The workflow includes:

- Data Preparation:

Preprocessing and cleaning the Twitter dataset to ensure it is suitable for analysis.

- Model Implementation:

Using HuggingFace's AutoModel and AutoTokenizer to build a sentiment analysis model.

- Sentiment Classification:

Implementing the model to accurately classify tweets as expressing either negative or positive sentiment.

Tools and Libraries

- HuggingFace Transformers:

For model building and tokenization.

- AutoModel:

To automatically load and fine-tune pre-trained models.

- AutoTokenizer:

For efficient text tokenization.