Training Sentiment Models

The end product of any sentiment analysis project is a sentiment model. This is an object containing a stored representation of the data on which it was trained. Such a model has the ability to predict sentiment values for text that it has not seen before. To develop a sentiment analysis model, the following steps need to be taken:

1. Split the document dataset into two, namely train and test datasets. The test dataset is normally a fraction of the overall dataset. It is usually between 5% and 40% of the overall dataset, depending on the total number of examples available. If you have a lot of data, then you can afford to have a smaller test dataset.
2. Preprocess the text by stripping unwanted characters, removing stop words, and performing other common preprocessing steps.
3. Extract the features by converting the text to numeric vector representations. These representations are used for training machine learning models.
4. Run the model's...