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Project Title
FinSent: Financial Sentiment Analysis for Market Predictions
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Timeline:
2.5 Months
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Tech Stack:
Python
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Demo Link:
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Client Name:
Project Detail:
FinSent is a comprehensive system designed to gauge market sentiment by analyzing vast amounts of text data from news articles, social media platforms, and other relevant sources. The primary goal is to equip investors and financial institutions with actionable insights derived from the prevailing sentiment to make informed investment decisions.
Problem Statement:
With the dynamic nature of the financial market, timely decision-making is crucial for investors. Traditional financial metrics, while important, may not capture sudden market shifts influenced by global events, industry news, or popular sentiment.
Solution:
We developed a sentiment analysis engine that scours the web for financial news, tweets, blog posts, and more. It processes this information in real-time, classifying sentiments as positive, negative, or neutral. This aggregated sentiment score, when combined with traditional metrics, offers a holistic view of the market mood, aiding in predictive analysis and risk management.
Features:
- Real-time Sentiment Scoring: Continuous evaluation of financial sentiments from multiple sources.
- Interactive Dashboards: Visualization tools for a quick assessment of the market mood.
- Alert System: Notification system for drastic sentiment shifts.
- Historical Sentiment Analysis: Archive and review past sentiments and correlate with market movements.
Use Cases:
- Investment Strategy: Tailor investment strategies based on prevailing market sentiment.
- Risk Management: Adjust financial portfolios in response to sudden negative sentiment spikes.
- Market Prediction: Anticipate market movements by correlating sentiment with historical market behavior.
Data Science Specific Points:
- Data Collection: Implemented web scraping tools to collect data from financial news websites, blogs, and social media platforms. Utilized APIs, where available, especially for platforms like Twitter.
- Data Analysis: Text data underwent preprocessing (tokenization, stemming, etc.) followed by sentiment classification using deep learning models and natural language processing techniques.
- Results: The system successfully identified sentiment trends in correlation with market movements, achieving an 87% accuracy rate in sentiment classification, leading to enhanced market prediction capabilities for our users.
Technologies Used:
- Programming Languages: Python, SQL
- Frameworks: TensorFlow, NLTK, spaCy
- Tools: Elasticsearch, Kibana, Web Scraping tools (BeautifulSoup, Scrapy)
- Databases: Google Cloud Databases