Sentiment Classification of News Articles

Our client, a mid-size financial services firm, relied on their analysts to manually scan hundreds of news articles each morning before markets opened. Analysts would read through headlines, skim articles, and try to gauge whether coverage of key holdings was positive, negative, or neutral. This process was slow, inconsistent, and heavily influenced by individual bias. Two analysts reading the same article would often come away with different sentiment readings. The firm needed a way to process news at scale with consistent, quantifiable sentiment scores that could feed directly into their trading models and risk dashboards.

We built a custom sentiment classification pipeline using Python and modern NLP techniques. The system ingests articles from multiple news APIs and RSS feeds, processes them through a multi-stage pipeline that handles text extraction, entity recognition, and context-aware sentiment scoring. Rather than simple positive/negative classification, the model produces granular sentiment scores on a continuous scale, broken down by entity (so a single article mentioning multiple companies gets per-company sentiment scores). We trained the model on a curated dataset of financial news annotated by domain experts, then validated it against historical analyst ratings. The system runs on AWS with auto-scaling to handle the morning news surge, and pushes results into the firm's existing dashboards via API.