Finding keywords
Counting known keywords is one of the first ways you can analyze text data in a Twitter dataset. In this dataset, you're going to count the number of times specific hashtags occur in a collection of tweets about data science. To this end, you're going to use the string methods in the pandas Series object to do this.
pandas and numpy have been imported as pd and np, respectively. A more fully-featured flatten_tweets and data_science_json have also been loaded for you.
This exercise is part of the course
Analyzing Social Media Data in Python
Exercise instructions
- Flatten the tweets with
flatten_tweets()and store them inflat_tweets. - Convert tweets to DataFrame using the pandas DataFrame constructor.
- Find mentions of
#pythonin'text', ignoring case. - Print proportion of tweets mentioning
#pythonby summingpythonwithnp.sum()and dividing it by the total number of tweets.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Flatten the tweets and store them
____ = ____(____)
# Convert to DataFrame
ds_tweets = ____.____(____)
# Find mentions of #python in 'text'
python = ____[____].____.____(____, ____)
# Print proportion of tweets mentioning #python
print("Proportion of #python tweets:", ____ / ____)