Text Analysis

Suppose our text data is currently arranged into a single file, where each line of that file contains all of the text in a single document. Here we can use SFrame.read_csv to parse the text data into a one-column SFrame.

import turicreate
sf = turicreate.SFrame('wikipedia_data')
X1      str

Rows: 72269

|               X1               |
| alainconnes alain connes i ... |
| americannationalstandardsi ... |
| alberteinstein near the be ... |
| austriangerman as german i ... |
| arsenic arsenic is a metal ... |
| alps the alps alpen alpi a ... |
| alexiscarrel born in saint ... |
| adelaide adelaide is a coa ... |
| artist an artist is a pers ... |
| abdominalsurgery the three ... |
|              ...               |
[72269 rows x 1 columns]
Note: Only the head of the SFrame is printed.
Text cleaning

First, we must convert our SFrame of str into an SFrame of type dict using turicreate.text_analytics.count_words. This operations converts the array of strings into an array of dictionaries. The keys are each word in the document and the values are the number of times the word occurs.

We can easily remove all words do not occur at least twice in each document using SArray.dict_trim_by_values.

Turi Create also contains a helper function called stop_words that returns a list of common words. We can use SArray.docs.dict_trim_by_keys to remove these words from the documents as a preprocessing step. NB: Currently only English words are available.

docs = docs.dict_trim_by_keys(turicreate.text_analytics.stopwords(), exclude=True)

To confirm that we have indeed removed common words, e.g. "and", "the", etc, we can examine the first document.

{'academy': 5,
 'algebras': 2,
 'connes': 3,
 'differential': 2,
 'early': 2,
 'geometry': 2,
 'including': 2,
 'medal': 2,
 'operator': 2,
 'physics': 2,
 'sciences': 5,
 'theory': 2,
 'work': 2}

results matching ""

    No results matching ""