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 should be taken:

1. The document dataset must be split into 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 the amount of data is too large, then a smaller test dataset can be used.
2. Next, the text should be preprocessed by stripping unwanted characters, removing stop words, and performing other common preprocessing steps.
3. The text should be converted to numeric vector representations in order to extract the features. These representations are used for training machine learning models...