Earnings_Call_Analyzed_By_NLP
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nilijing
Earnings Call Sentiment Analysis. This repository includes my work on extracting the focus area of companies from their earnings calls transcripts.
Earnings_Call_Analyzed_By_NLP
Collect companies' Earnings Call Transcripts by web scraping and then apply NLP methods to their contents to find the best sentiments analyser.
To test this project, we will apply Microsoft Corporation earnings call as an example.
Data Source and Pacakages
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US Corporate Earnings Call Transcripts can be reached from https://news.alphastreet.com/
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Stanford NER is a Java implementation of a Named Entity Recognizer, the package we used in this project can be download from https://drive.google.com/drive/folders/1mS0y92w65f4R7u6dNP8KFFAtdmNmI-v9?usp=sharing the file named 'stanford-ner'
Four NLP analysis methods
- FinBERT
- PySentiment
- TextBlob
- Vader
Results
Finbert enjoys high accuracy and an acceptable error rate at the same time.
WordCloud plot shows that 'currency', 'growth','revenue','cloud' are the most common words mentioned in the latest 12 Microsoft earning calls.
Reference
FinBERT model introduction：https://paperswithcode.com/paper/finbert-a-pretrained-language-model-for
nilijing