Recipes
A common requirement in extracting information is the ability to identify all persons or characters referred to in the text. It is an elaborate process of knowing parts-of-speech in the text,...
Stemming and Lemmatization are text analysis methods that return the root word of derivative forms of the word. This is done by removing the suffixes of words (stemming) or by comparing the...
This section presents a concise summary of what the recipe will teach, focusing primarily on: (1) the outcome of the recipe (what are you trying to achieve, in non-technical terms); (2) the main...
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...
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...
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...
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...
This recipe helps you explore how to analyze a text based on its parts of speech (e.g. nouns, adjectives, prepositions, etc.).
This recipe uses Python and WordNet to explore meaning in a text.