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  3. Spoken Language Processing in Python

Exercise

Analyzing sentiment of a phone call

Once you've transcribed the text from an audio file, it's possible to perform natural language processing on the text.

In this exercise, we'll use NLTK's VADER (Valence Aware Dictionary and sEntiment Reasoner) to analyze the sentiment of the transcribed text of call_2.wav.

To transcribe the text, we'll use the transcribe_audio() function we created earlier.

Once we have the text, we'll use NLTK's SentimentIntensityAnalyzer() class to obtain a sentiment polarity score.

.polarity_scores(text) returns a value for pos (positive), neu (neutral), neg (negative) and compound. Compound is a mixture of the other three values. The higher it is, the more positive the text. Lower means more negative.

Instructions

100 XP
  • Instantiate an instance of SentimentIntensityAnalyzer() and save it to the variable sid.
  • Transcribe the target call and save it to call_2_text.
  • Print the polarity_scores() of call_2_text.