Sentiment Analysis on Tweets using Support Vector Machines and data set of 10,000 tweets.
Sentiment classi cation has become a prevalent topic because of its various usages for com- panies. In this assignment, I am given a basic sentiment classi er (sentiment analyzer) and asked to modify it with additional features. The base model achieves a precision of 0.7112 and recall of 0.7461 for a F1 score of 0.7283. My improved model achieves a precision of 0.8359 and recall of 0.8333 for a F1 score of 0.8346.
- Open python terminal (3.+)
- type > python train_model.py