Visualization

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

In this recipe, we measure a corpora to determine authorship of the featured texts and visualize them by authorship. We will use Multidimensional scaling(MDS) as one of the techniques for analyzing similar / dissimilar data. This recipe if based on Jinman Zhang's Cookbook on Github.

This recipe with show you how to prepare Voronoi diagrams, one way of showing relationships between words in a text and a search term. In order to do this, we will employ the use of word embeddings. These represent individual words in a text as real-valued vectors in a confined vector space. This recipe is based on Kynan Ly's cookbook as seen on this notebook.

Multidimensional Scaling (MDS) is a method to convert sets of document terms into a data frame that can then be visualized. The distances expressed in the visualization show how similar, or dissimilar, the contents of one text are to another. This recipe deals with several advanced text analysis concepts and methods. Links are provided to additional information on these terms.

Panda Data frame: https://pandas.pydata.org/

TF-IDF: https://en.wikipedia.org/wiki/Tf%E2%80%93idf

Principal Component Analysis (PCA) is a method to convert sets of document terms into a data frame that can then be visualized. The distances expressed in the visualization show how similar, or dissimilar, the contents of one text are to another. PCA tries to identify a smaller number of uncorrelated variables, called "principal components" from the dataset. The goal is to explain the maximum amount of variance with the fewest number of principal components.  This recipe deals with several advanced text analysis concepts and methods.

In this recipe we use 3 ebooks to show how topic analysis can identify the different topics each text represents. We will use Latent Dirichlet Allocation (LDA) approach which is the most common modelling method to discover topics. We can then spice it up with an interactive visualization of the discovered themes. This recipe is based on Zhang Jinman's notebook found on TAPoR.

NB: Any number of texts can be used, we choose 3 for this recipe.

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.

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