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In this video, I describe a cryptocurrency headline sentiment analyzer that scrapes headline news from Google news and performs inference on these headlines to determine whether those headlines are positive, negative or neutral. A cryptocurrency headline sentiment analyzer is a type of natural language processing (NLP) tool that is designed to analyze the sentiment of news headlines related to cryptocurrencies. With the rise of cryptocurrencies, there has been a significant increase in the number of news articles and headlines related to the topic. These news articles can have a significant impact on the cryptocurrency market, as positive or negative news can affect the price and trading volume of cryptocurrencies. A cryptocurrency headline sentiment analyzer uses machine learning and NLP techniques to automatically analyze news headlines and classify them as positive, negative, or neutral in terms of sentiment. This is typically done using a combination of rule-based methods and machine learning algorithms, such as logistic regression, support vector machines, or neural networks.
The output of a cryptocurrency headline sentiment analyzer can be used by investors, traders, and other stakeholders in the cryptocurrency market to gain insights into market sentiment and make informed decisions about trading and investment strategies.

🛠️🛠️ Tools
Weights & Biases – https://wandb.me/bazzi
Pygooglenews – https://github.com/kotartemiy/pygooglenews
FinBERT via HuggingFace – https://huggingface.co/ProsusAI/finbert
PyTorch – https://pytorch.org/
Google Colab – https://colab.research.google.com/

⏲⏲Outline
00:00 Intro
02:59 Installing wandb
03:43 Importing wandb
04:02 Scraping headlines the bs4 way (optional)
08:48 Scraping Google News headlines by pygooglenews
14:23 Training via FinBERT
18:02 Inference via PyTorch
20:45 Initialize wandb tables
23:33 Store inferred data in wandb tables
25:44 Tracking system performance
26:28 Pushing wandb tables
27:50 Creating customizable columns in wandb
29:06 Histogram distributions of crypto headline sentiments on wandb
32:19 Averaging headline sentiment scores on wandb
34:14 Visualizing data on scatterplots in wandb
37:34 Outro

💰💰Mentioned Cryptos:
Bitcoin – https://bitcoin.org/
Ethereum – https://ethereum.org/
Cardano – https://cardano.org/
Safemoon – https://safemoon.net/
Litecoin – https://litecoin.com/
Fantom – https://fantom.foundation/
NFT – https://ethereum.org/en/nft/

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🎥🎥 Visual Credits
Thank you for the following designers and video makers from  @Pexels  for making this video possible
Tima Miroshnichenko
Ewan Kennedy 
Gleb Dolskiy
Stef
iMotivation 

🎵 Audio Credits
Composer: Zen Man
Thank you so much !
Music: https://pixabay.com/music/ambient-modular-ambient-04-792/

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WHO AM I:
I am an Electrical Engineer and YouTuber w/ a PhD in Electrical Engineering from @EURECOM Communication Systems Department , Sophia Antipolis in France. I am also a EURECOM alumni, w/ a Masters studies from  @CentraleSupélec  (Summa Cum Laude). I have many publications in well-known  @IEEEorg  conferences, including a nomination award and is a co-inventor in several patents. I am also a former member of  @IEEE Signal Processing Society . I also dedicate time to publish high level lectures on Mathematics (including convex optimization) and Programming. I also focus on Math for Machine Learning, Convex Optimization, Linear Algebra, Python, SymPy, NumPy, Pandas, CVXOPT, MATLAB, and more.

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