Natural Language Processing

This recipe is part of the Text Analysis for Twitter Research (TATR) series, and will look at tokenizing and extracting key features from a Tweet.

This recipe is part of the Text Analysis for Twitter Research (TATR) series. This recipe will describe Panda dataframe manipulation, in particular the techniques used for some of the more advanced Twitter analysis found in the TATR library.

This recipe is part of the Text Analysis for Twitter Research (TATR) series. The recipe will show how to load and save a CSV (comma-separated values) file into a Panda data structure.

This recipe is part of the Text Analysis for Twitter Research (TATR) series and describes how to begin plotting basic graphs using Twitter data.

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.

Multiple Correspondence Analysis (MCA) is a data analysis technique that can detect and represent the underlying structures of a dataset. In terms of textual analysis, we can identify and graph simultaneously occurring variables from the texts that comprise a corpus.

You can retrieve the MCA module at https://pypi.python.org/pypi/mca/1.0.3, or by typing the following line in the Python terminal:

pip install --user mca

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.

This recipe will show you how to classify text into general topics. We will use supervised machine learning model called Support Vector Machines (SVM) and the 20 Newsgroups subject matter data set as the topic classifier. This recipe is based on Jinman Zhang's Cookbook.

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