Coloring by sentiment
We want to be able to differentiate by place with our Twitter analysis. One distinguishing factor between places is how the State of the Union speech was received. For this purpose, we'll use the sentiment analysis we covered in Chapter 2 to evaluate how the speech was received in different parts of the country.
The tweets_sotu dataset has been loaded for you, as well as lon, lat, and the Basemap map m. SentimentIntensityAnalyzer is instantiated as sid in your workspace.
Este ejercicio forma parte del curso
Analyzing Social Media Data in Python
Instrucciones del ejercicio
- Calculate the sentiment scores and store them.
- Draw the points, setting the color argument to
sentiment_scoreand the colormap to 'coolwarm'.
Ejercicio interactivo práctico
Prueba este ejercicio completando el código de muestra.
# Generate sentiment scores
sentiment_scores = ____[____].apply(____)
# Isolate the compound element
sentiment_scores = [x['compound'] for x in sentiment_scores]
# Draw the points
____.____(____, ____, ____ = True,
c = ____,
____ = 'coolwarm', alpha = 0.7)
# Show the plot
plt.show()