We aim to analyze and understand the sentiments expressed in financial news headlines, particularly from the perspective of retail investors. The dataset provides valuable information with two key columns: 'Sentiment' and 'News Headline.' The 'Sentiment' column categorizes each headline as negative, neutral, or positive, reflecting the sentiment conveyed in the news. Our objective is to leverage natural language processing and machine learning techniques to perform sentiment analysis on this dataset.
We will help mentees in :-
We will explore the dataset, examining the distribution of sentiment labels and gaining insights into the types of financial news headlines it contains.
We will preprocess the 'News Headline' text data by performing tasks such as text cleaning, tokenization, and stemming/lemmatization.
We will employ machine learning and natural language processing models to classify news headlines into their respective sentiment categories (negative, neutral, or positive).
We will evaluate the performance of our sentiment analysis model using appropriate metrics such as accuracy, precision, recall, and F1 score. This will help us assess the model's effectiveness in predicting sentiment.
We will provide insights into the sentiments expressed in financial news headlines and how they may impact retail investors. Additionally, we will analyze any notable trends or patterns in the data.