Text Gathering

This recipe is part of the Text Analysis for Twitter Research (TATR) series. The recipe will look at categorizing text using the General Inquirer Categories released by Harvard

This recipe is part of the Text Analysis for Twitter Research (TATR) series. In this recipe we will show you how to use a dataset of Tweets to find the most popular hashtags by date. The results can then be manipulated by placing them in a Panda dataframe and visualized by plotting the most popular hashtag points over time.

This recipe uses regular expressions (or Regex) to clean a text document. This recipe is based on the Using Regular Expressions to Clean a Text code.

This recipe will use regular expressions to clean up a webpage. This is useful if you want to carry out any meaningful textual analysis of the content in a web page. We can remove the html tags and other unnecessary textual elements with this method.

Word frequencies and counts are text analysis methods that return results about the words in a text or set of texts. Counts return the amount of times a word is used in the text, whereas frequencies give a sense of how often a word is used in comparison to others in the text.

Tokenization is the process of splitting a sentence or a chunk of text into its constituent parts. These “tokens” may be the letters, punctuation, words, or sentences. They could even be a combination of all these elements. This recipe was adapted from a Python Notebook written by Kynan Lee.

This recipe shows how to scrape comments from a YouTube video to analyze.

This utility is for creating a simple web scraper with Python.