_In a [previous post] we set out to explore the dataset provided by the [Trump Twitter Archive]. This is part two of the Exploring Trump series.On this post, we’ll continue our journey but this time we’ll be using spaCy.

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For this project, we’ll be using pandas for data manipulation, spaCy for natural language processing, and joblib to speed things up.Let’s get started by firing up a Jupyter notebook!

Housekeeping

Let’s import pandas and also set the display options so Jupyter won’t truncate our columns and rows. Let’s also set a random seed for reproducibility.

# for manipulating data
import pandas as pd

# setting the random seed for reproducibility
import random
random.seed(493)
# to print out all the outputs
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# set display options
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', -1)

Getting the Data

Let’s read the data into a dataframe. If you want to follow along, you can download the cleaned dataset here along with the file for stop words¹. This dataset contains Trump’s tweets from the moment he took office on January 20, 2017 to May 30, 2020.

df = pd.read_csv('trump_20200530_clean.csv', parse_dates=True, index_col='datetime')

Let’s take a quick look at the data.

df.head()
df.info()

Using spaCy

Now let’s import spaCy and begin natural language processing.

# for natural language processing: named entity recognition
import spacy
import en_core_web_sm

We’re only going to use spaCy’s ner functionality or named-entity recognition so we’ll disable the rest of the functionalities. This will save us a lot of loading time later.

#nlp #spacy #exploring-trump #data-science #data analysis #data analysis