Sentiment Analysis of earnings call transcripts is a great analysis. In this video, we used TextBlob and FMP API to build a model. Click โ€œShow moreโ€ to learn more:

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Important Links:
TextBlob: https://textblob.readthedocs.io/en/dev/
TextBlob Pypi: https://pypi.org/project/textblob/
FMP blog on Analyze Earnings Call Transcripts: https://site.financialmodelingprep.com/education/how-to-access-and-analyze-earnings-call-transcripts?direct=true
FMP blog on Earnings Call Natural Language Processing Analysis : https://site.financialmodelingprep.com/how-to/how-to-use-natural-language-processing-to-analyze-earning-call-transcripts-with-python
Financial Modeling Prep API Documentation: https://site.financialmodelingprep.com/developer/docs/
Earnings Calendar: https://site.financialmodelingprep.com/developer/docs/earnings-calendar-api/#Python
Earnings Surprises: https://site.financialmodelingprep.com/developer/docs/earnings-surprises-api/#Python
Earnings Call Transcripts: https://site.financialmodelingprep.com/developer/docs/earning-call-transcript-api/

TextBlob: It is a simple python library that offers API access to different NLP tasks such as sentiment analysis, spelling correction, etc.
TextBlob sentiment analyzer returns two properties for a given input sentence:
โ€ข Polarity is a float that lies between [-1,1], -1 indicates negative sentiment and +1 indicates positive sentiments.
โ€ข Subjectivity is also a float which lies in the range of [0,1]. Subjective sentences generally refer to personal opinion, emotion, or judgment.
Textblob will ignore the words that it doesnโ€™t know, it will consider words and phrases that it can assign polarity to and averages to get the final score.

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TimeStamps:
Introduction (0:00)
Topics to cover (1:34)
Financial Modeling Prep API Documentation (2:22)
TextBlob Documentation (4:28)
Earning Calendar API (5:54)
Earnings Surprises API (8:06)
Python for Financial Programming (9:19)
Earnings Call Transcript API (10:30)
TextBlob Sentiment Analysis (11:40)
My Google Drive Access (15:55)
Outro (17:00)

#sentimentanalysis #finance #python