Social media platforms like Twitter have become a significant source of real-time information and public opinion. Analyzing the sentiment of tweets can provide valuable insights into people's opinions and emotions towards various topics, brands, or events. Twitter sentiment analysis involves automatically classifying tweets into positive, negative, or neutral sentiment categories.
The goal of this project is to develop a sentiment analysis model using machine learning techniques to classify tweets based on their sentiment. The model will be trained on a labeled dataset of tweets.
The dataset used is a collection of sentiment data from twitter users during the implementation of the 2019 presidential election. It consists of 1815 tweet data containing three sentiment categories, namely: Positive, Neutral and Negative.