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Financial News and Social Sentiment Analysis System for Stock Market Insights

We developed a market intelligence platform that analyzes financial news, social media trends, and stock price movements to identify market sentiment and trading opportunities in real time.
 The system combines natural language processing, social data mining, and financial analysis to help traders and analysts understand how market perception impacts stock performance.

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Summary :

We have built an AI-based system that continuously tracks financial news, trending social topics, and stock movements to deliver sentiment-driven market insights.
Using NLP and data aggregation pipelines, the system evaluates sentiment from both verified news portals and real-time social chatter on platforms like Twitter/X.
It correlates this sentiment with live stock price trends, enabling early signal detection and informed investment decisions.

Problem

Problem

Objectives

  • Centralize Market Intelligence

    Aggregate data from financial news, social media, and market feeds.

  • Automate Sentiment Evaluation

    Classify sentiment polarity in real time using AI.

  • Detect Early Market Signals

    Correlate trending topics with stock price shifts.

  • Enhance Trading Decision Support

    Deliver sentiment-based indicators and summaries.

  • Scale Monitoring Across Stocks

    Support multi-stock, multi-source real-time processing.

Challenges

  • Processing high-frequency social data with noise and slang.
  • Detecting genuine trends vs. irrelevant social buzz.
  • Maintaining consistency between news sentiment and market sentiment.
  • Handling real-time stock price API synchronization.
  • Visualizing correlations intuitively for traders and analysts.

Solution

We developed a real-time financial sentiment analysis system that combines data from both traditional media and social networks.

The platform scrapes financial news headlines, monitors trending discussions on Twitter/X, and analyzes tone using LLMs through LangChain orchestration.

It integrates live stock data from APIs, mapping sentiment fluctuations against price movement to identify leading indicators of volatility.

The Streamlit dashboard visualizes sentiment trends, hot topics, and correlated stock responses , enabling investors to react faster to market signals.

Technologies

Architecture

Data Collection
Scrape news, tweets, and stock prices from verified APIs and web sources.
Sentiment Classification
AI model classifies tone (positive, negative, neutral) by entity and topic.
Trend Detection
Identify trending hashtags, companies, or tickers from social data.
Stock Correlation
Map sentiment changes to price movement and volatility metrics.
Visualization & Insights
Dashboard shows live sentiment heatmaps, top mentions, and sentiment–price correlation charts.

Results & Impact

Correlated market sentiment with real-time stock movements

Improved trading intelligence through combined news and social media analytics

Automated analysis of financial headlines and trending topics

Reduced manual research time by 80%

Enabled multi-source monitoring for dynamic market perception tracking

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