My name is Matthew Schwartz and I'm the developer and sole owner of SocialSentiment.io. What started as an experiment in machine learning (ML), natural language processing (NLP), and sentiment analysis turned into the site you see here. It's a fun project to work on and I hope you find it useful.
SocialSentiment.io grew out of an experiment. I was curious what information I could gather from sentiment analysis of tweets related to stocks. Do tweets affect stock price? Can we predict stock price movement from public sentiment on social media? Does sentiment change before, during, or after stock price swings?
I started SocialSentiment.io with a somewhat simplistic machine learning algorithm. I defined a recurrent neural network to perform sentiment analysis of short texts from social media. Its purpose, and therefore its training set, is focused on the topic of stocks, companies, and their products. It returned only one floating point value representing a single prediction for each string. I quickly fell into a common ML natural language processing trap: new text which is off-topic returns unpredictable and unhelpful results. Worse yet, the results don't directly indicate the text it analyzed is off-topic.
When we started SocialSentiment.io we focused entirely on sentiment for our social media post analysis. Using thousands of real posts as training inputs to our neural networks we got back predictions as floating point numbers between 0 and 1. Each post was calculated to be leaning negative (toward 0) or positive (toward 1) by our machine learning model. We also trained with neutral posts leading to predictions around 0.5. These predictions are aggregated along with post popularity to calculate social sentiment scores for publicly traded stocks.
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