Fundamentals of NLP: Rule-based Models for Sentiment Analysis

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Sentiment Analysis is a common use-case within the discipline of Natural Language Processing (NLP). Here, a model attempts to understand the contents of a text document well enough to capture the feelings, or sentiments, conveyed by the text. Sentiment Analysis is widely used by political forecasters, marketing professionals, and hedge fund managers looking to spot trends in voter, user, or market behavior. You will start this course by loading and preprocessing your data. You will read in data on movie reviews from IMDB and explore the dataset. You will then visualize the data using histograms and box plots to understand review length distribution. After that, you will perform basic data cleaning on text, utilizing regular expressions to remove elements like URLs and digits. Finally, you will conduct sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob.