Assignment
Our client, a finance firm, tasked us with a challenging project: to scrape tweets related to the top 50 companies listed on the New York Stock Exchange (NYSE) over a span of three years. The goal was to conduct sentiment analysis on these tweets and evaluate their potential influence on the stock prices of these companies.
Scope
Approach
To address the complex requirements of the project, we adopted a comprehensive approach:
Data Scraping: We developed custom Python scripts to scrape tweets from various sources, ensuring that we collected a substantial dataset for each of the 50 companies.
Sentiment Analysis: Natural Language Processing (NLP) techniques were applied to analyze the sentiment of the tweets.
Stock Price Data: We obtained historical stock price data for the 50 companies, allowing us to correlate sentiment analysis results with stock price movements.
Technologies
Outcome
The results of the project yielded valuable insights:
Sentiment Trends: We identified sentiment trends in tweets related to each company over the three-year period, enabling us to gauge public sentiment towards these companies.
Correlation Analysis: By correlating sentiment analysis results with stock price movements, we determined whether there were any statistically significant relationships between sentiment and stock price changes.
Investment Insights: The project provided investors with valuable information regarding the potential impact of public sentiment on the stock prices of NYSE-listed companies.
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