Next, we need to preprocess the text to convert it into a format that can be processed for extracting information. It is essential to reduce the size of the feature space before analyzing the text. There are various preprocessing methods that we can use here, such as stop word removal, case folding, stemming, lemmatization, and contraction simplification. However, it is not necessary to apply all of the normalization methods to the text. It depends on the data we retrieve and the kind of analysis to be performed.
The series starts off with a quick description of some preprocessing steps and then building an LDA model to extract key terms from articles.