Natural Language Processing

Conducting sentiment analysis on a chosen dataset, including EDA, text preprocessing, and feature engineering to understand the results.

Implementing text classification using various information retrieval methods, analyzing, comprehending, and comparing the techniques used.

Implementing text preprocessing operations such as case conversion, tokenization, stop word removal, stemming, lemmatization, and POS tagging using NLTK. Additionally, understanding the difference between stemming and lemmatization, and counting word frequencies.

Implementing POS tagging and Named Entity Recognition (NER) techniques to extract structured information from text data.