Background: Twitter, as a prominent social media platform, serves as a valuable source of public opinions, sentiments, and reactions to various topics, products, and events. Sentiment analysis of tweets using machine learning techniques, such as Support Vector Machines (SVM), allows us to automatically classify the sentiments expressed in tweets as positive, negative, or neutral. This analysis provides insights into public perceptions and opinions, which can be crucial for businesses, brands, and researchers.
Problem Statement: Develop a sentiment analysis model using Support Vector Machines to classify the sentiments expressed in tweets as positive or negative.
Objective: Build and evaluate a predictive model that can accurately classify the sentiments of tweets using SVM, providing valuable insights into public opinions and reactions on Twitter.
Dataset: The Sentiment140 Twitter Dataset comprises 1.6 million labeled tweets, serving as a fundamental resource for sentiment analysis research. Each tweet is categorized as either positive or negative sentiment, making it a valuable asset for training and evaluating sentiment classification models.