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.
Similar to finding People and Characters, finding locations in text is a common exploratory technique. This recipe shows how to extract places, countries, cities from a text. We will use Named-Entity Recognition (NER) module of NLKT library to achieve this. This recipe is based on Jinman Zhang's cookbook.
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.
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 for TAPoR.ca.
This recipe discusses ways to find electronic texts (e-texts) online that can be used by other text analysis tools.
Compare word usage across two corpora to see if there is any difference between the two. Look at a word in the two texts, you use the U test to determine if the difference is significant. Helps determine the uniqueness of a term.
Term Frequency-Inverse Document Frequency or TF-IDF, is used to determine how important a word is within a single document of a collection. It will help determine the importance or weight of word to a document in a collection or corpus. It ranks the importance of word based on how often it appears in a text but the rank is offset by how often it occurs in the whole collection.
This recipe explores how to analyze a corpus for the locations that are written about within it. These results can be mapped to visualize the spatial focus of the corpus. This recipe is based off of an iPython notebook by Matthew Wilkins.
This recipe uses Python and the NLTK to explore repeating phrases (ngrams) in a text. An ngram is a repeating phrase, where the 'n' stands for 'number' and the 'gram' stands for the words; e.g. a 'trigram' would be a three word ngram.