NLP on Articles - First Task

[theDe-bugger]

Description

Kind of basic but follow a tutorial or train your own NLP model to perform sentiment analysis on articles related to certain stocks. You can simply train a model on normal articles and then use it to perform analysis on newer articles related to a specific stock of your choice.

Requirements

• Use an api or download a dataset for analysis
• Read and preprocess the data as needed
• Visualize the data to identify any trends (if needed)
• Train your model, present accuracy/findings
• Put everything in one clean analysis jupyter notebook under ml/nlp/yournameanalysis.ipynb <- replace with your file name (make sure it includes your name)
• Add documentation, a pdf of your summary of findings, and organize the repo so it's readable and clean.

Resources/Tools

• https://www.newscatcherapi.com/blog/sentiment-analysis-using-python
• This has a similar idea but for twitter tweets. Feel free to research and find more resources online, there's tons of them.
• look at YouTube, Kaggle, and other online resources, there are a ton of them that show how to apply nlp in python and the general idea as well

Specifications

• first ticket, get it done quick, shouldn't take too long as it's a follow along :)