Fundamentals of Text Analytics: Suggested Sequence

The following list represents a suggested sequence for the text analytics teaching tools. The sequence is designed to ensure that students build upon their previous learning as they progress through the topics.

Topic	Classroom Tools
I. Text Analysis: Basics	Introduction to Text Analysis An overview of natural language processing (NLP), concentrating on techniques used to prepare text data for analysis.
II. Sentiment Analysis	Introduction to Sentiment Analysis Learn basics of sentiment analysis, including how text can be classified as positive, negative, or neutral. Sentiment Analysis Lexicons Introduces different types of sentiment lexicons, including a domain-specific sentiment lexicon used to analyze financial statements. Challenges of Sentiment Analysis Examines common challenges to sentiment analysis, and includes an introduction to the VADER model for performing a sentiment analysis.
III. Named-Entity Recognition	Named-Entity Recognition Understand the practice of named-entity recognition (NER) and evaluate three different approaches to NER.
IV. Machine Learning	Machine Learning: Text Classification An overview of supervised machine learning, and a step-by-step explanation of how a naive Bayes classifier works for text classification. Unsupervised Machine Learning: Clustering Learn about clustering as an unsupervised machine learning task and become familiar with how the k-means algorithm works for text classification. Introduction to Topic Modeling An introduction to the benefits and challenges of using topic modeling, as well as an overview of the latent Dirichlet allocation (LDA) method.. Topic Modeling: NMF Begins with a short review of topic modeling and moves on to an overview of a technique for topic modeling known as non-negative matrix factorization (NMF).