Sentiment_analysis

Overview

This repository contains code and resources for a sentiment analysis project. Sentiment analysis is a natural language processing (NLP) task that involves determining the sentiment expressed in a piece of text, such as positive, negative, or neutral.

Features

Sentiment Analysis Models:

Utilizes state-of-the-art pre-trained models for sentiment analysis. Integration with Hugging Face Transformers library for easy model loading.

Models that is used:
"cardiffnlp/twitter-roberta-base-sentiment"

Sentiment Analysis Pipeline:

Provides a user-friendly pipeline for quick sentiment analysis on new text inputs. Handles tokenization, model inference, and result interpretation seamlessly.

Data Analysis and Visualization:

Utilizes pandas, numpy, matplotlib, and seaborn for data analysis and visualization. Provides insights into the distribution of sentiment scores in the dataset.

Libraries Used

pandas
numpy
matplotlib
seaborn
nltk
transformers
scipy

Acknowledgments

The sentiment analysis models used in this project are based on Hugging Face Transformers.
Special thanks to the open-source community for their contributions and support.