This project aims to perform sentiment analysis on a dataset of 1 million tweets using BERT (Bidirectional Encoder Representations from Transformers) from TensorFlow in the Python language. The objective is to classify the sentiment of each tweet as positive, negative, or neutral.
Sentiment analysis is a natural language processing technique used to determine the sentiment or emotion expressed in text data. In this project, we leverage the power of BERT, a state-of-the-art transformer-based model, to analyze the sentiment of a large dataset consisting of 1 million tweets.
By applying BERT to the dataset, we can obtain fine-grained sentiment predictions for each tweet. The project involves preprocessing the text data, fine-tuning the BERT model on the dataset, and evaluating its performance. The ultimate goal is to create a robust sentiment analysis model that can accurately classify the sentiment of tweets at scale.
