"Sentiment Analysis of Financial Headlines" aims to uncover valuable insights from financial news language. Using advanced Natural Language Processing (NLP) techniques, we'll analyze the feelings expressed in headlines from top financial news sources. The goal is to understand whether the overall sentiment is positive, negative, or neutral, helping investors and analysts make more informed decisions.
Performed preprocessing techniques for cnbc, guardian, reuters datasets which are available on kaggle.
-Linear SVC, a support vector machine gave an accuracy of 93.43%
-Logistic Regression gave an accuracy rating of 89.37%
-Bernoulli Model came with an accuracy of 73.61%