This chapter will explore the concept of text analysis and how it can be interpreted through visualizations. First, we will look at the data set and its components and then merge it to another data frame to have a more precise and accurate data frame. We will then discuss how we can use various methods with data frames to clean up our data for better analysis. We will then shift our focus to identifying and graphing relationships between multiple variables in a data frame. Finally, we will focus on evaluating and interpreting text to determine whether the text conveys a positive, negative, or neutral sentiment.
8.1.1. Learning Goals¶
Analyze and measure text complexity
Find relationships in, and make a visual representation, from the text.
Tidy up data to create a proper format for analysis
Graph the relationship between different variables in a data set
8.1.2. Learning Objectives¶
Reshape and merge one data frame to another to create a more precise and consistent data frame
Apply the basic principles of tidying up data
Measure text complexity using the Python package Textatistic
Score and interpret various text using Natural Language ToolKit (NLTK)