Sentiment for AAPL
We were inspired to build Sentimeter when we saw how inaccurate Twitter sentiment of specific stock were in determining their prices. We were determined to build a model to beat the industry standard through flitering market noise.
What it does
Sentimeter is a web application to analyze and display the sentiment of stocks. The majority of the project was computer science and data science research. We were able to build a system to read and understand the sentiment of phrases in articles. After weighting these sentiments based on the trust flow of each article source, we can accurately determine the true sentiment and "market hype" for a specific stock.
How I built it
Built using R and Python for Data Analysis and node.js on backend.
Challenges I ran into
Accomplishments that I'm proud of
What I learned