Financial Sentiment Classifier using SBERT
Extended Description
Overview
In the realm of financial decision-making, timely and accurate information is crucial. One of the key aspects of understanding the market and its behavior is analyzing the sentiment of financial news, articles, and social media posts. Positive or negative sentiment in financial news can have significant impacts on stock prices, investment decisions, and even market trends.
This model, the Financial Sentiment Classifier using SBERT, is designed to classify the sentiment of financial news headlines into three categories: positive, negative, and neutral. The model utilizes Sentence-BERT (SBERT), a transformer-based model, which creates dense and rich embeddings from sentences. These embeddings are then passed through a RandomForestClassifier, which classifies the sentiment based on historical data of financial news headlines.
SBERT is an ideal choice for this task because it is specifically fine-tuned for sentenc…
