In order to comprehend how the text is organised, syntactic and semantic methods were being used (to identify meaning). Lemmatization, tokenization, and part-of-speech tagging are some of these approaches.After the text has been cleaned up using NLP methods, machine learning algorithms may classify it. Computers could now detect patterns in data and forecast events thanks to machine learning. So instead explicit instructions, machine learning algorithms get their cues from example that are close to them (training data). If you desire your model to be able to classify text according to sentiment, you must train it with examples of textual emotions.