Learn practical skills, build real-world projects, and advance your career

Sentiment Analysis


So far, all of the analysis we've done has been pretty generic - looking at counts, creating scatter plots, etc. These techniques could be applied to numeric data as well.

When it comes to text data, there are a few popular techniques that we'll be going through in the next few notebooks, starting with sentiment analysis. A few key points to remember with sentiment analysis.

  1. TextBlob Module: Linguistic researchers have labeled the sentiment of words based on their domain expertise. Sentiment of words can vary based on where it is in a sentence. The TextBlob module allows us to take advantage of these labels.
  2. Sentiment Labels: Each word in a corpus is labeled in terms of polarity and subjectivity (there are more labels as well, but we're going to ignore them for now). A corpus' sentiment is the average of these.
    • Polarity: How positive or negative a word is. -1 is very negative. +1 is very positive.
    • Subjectivity: How subjective, or opinionated a word is. 0 is fact. +1 is very much an opinion.

For more info on how TextBlob coded up its sentiment function.

Let's take a look at the sentiment of the various transcripts, both overall and throughout the comedy routine.

Sentiment of Parvas

# We'll start by reading in the corpus, which preserves word order
import pandas as pd

data = pd.read_pickle('data_clean.pkl')