Sentiment Analysis from Financial News AI Full Syllabus

Module 1: Introduction to Sentiment Analysis in Finance

• What is sentiment analysis?
• Importance of sentiment in financial markets
• Use-cases: Stock prediction, volatility estimation, event impact
• Limitations of traditional technical indicators

Module 2: Financial News Sources & Data Collection

• Real-time data from:
  • Yahoo Finance
  • Google News RSS feeds
  • Twitter (financial hashtags)
  • News API, Finnhub, Bloomberg API
• Web scraping using BeautifulSoup & Selenium
• Handling JSON/XML news feeds

Module 3: Data Cleaning & Preprocessing

• Removing stop words, punctuation, numbers
• Tokenization and Lemmatization
• Named Entity Recognition (NER) for company names
• Extracting relevant keywords from headlines

Module 4: Natural Language Processing Basics

• Bag of Words, TF-IDF, and Word Embeddings
• Word2Vec, GloVe, FastText embeddings
• Handling financial jargon & domain-specific terms
• Creating custom finance dictionaries

Module 5: Rule-based Sentiment Models

• VADER (Valence Aware Dictionary)
• TextBlob for polarity & subjectivity
• Loughran-McDonald Financial Sentiment Dictionary
• Scoring financial headlines manually

Module 6: Machine Learning for Sentiment Classification

• Binary and multiclass sentiment (Positive/Neutral/Negative)
• ML models:
  • Logistic Regression
  • Naive Bayes
  • Random Forest
• Model training, validation & confusion matrix

Module 7: Deep Learning-Based Sentiment Analysis

• Recurrent Neural Networks (RNN) and LSTM
• Bidirectional LSTM (BiLSTM)
• Convolutional Neural Networks for text
• Combining multiple news items for time-based context

Module 8: Advanced Financial NLP Models

• FinBERT (pretrained BERT for financial text)
• Zero-shot classification with HuggingFace
• Using transformers to understand news impact
• Fine-tuning language models on your own dataset

Module 9: Correlating Sentiment with Market Movements

• Mapping headlines to stock price movement
• Lag-based analysis (event → price shift)
• Sentiment scores vs stock returns
• Visualizing news impact on charts

Module 10: Building a Sentiment Dashboard

• Frontend with Streamlit / Flask
• Real-time news sentiment feed
• Visualization: Sentiment trends, heatmaps, word clouds
• Trigger alerts based on sentiment thresholds

Module 11: Automating Sentiment-Powered Strategies

• Integrating sentiment signals with trading bots
• Sentiment + Technical indicators strategy
• Trade execution via broker APIs
• Paper trading & live backtests

Module 12: Risk, Ethics & Bias in AI Sentiment

• Biased datasets and model hallucination
• Fake news and misleading headlines
• Legal risks in trading based on news
• Responsible deployment of sentiment AI