Using Machine Learning to Classify Tweets

I recently had the opportunity to take on a project with Inspirit AI where I worked with a team to use machine learning to classify whether tweets were considered positive, negative, or neutral as they related to different stocks. In order to do that we explored three different machine learning models for classifying text: bag-of-words, long short-term memory (LSTM), and bidirectional encoder representations from transformers (BERT). Here I describe our experience in solving this problem and highlight what we learned from using the different methods. The bag of words model works by grouping each word into a bag or frequency count based on how often the word is used. The frequency count can be used as a feature in a machine learning model.