https://github.com/chengetanaim/sentimentanalysisforfinancialnewsnotebook

Building the model of a financial news sentiment classifier. Financial news headlines will be classified as positive, negative or neutral (from an investor point of view)
https://github.com/chengetanaim/sentimentanalysisforfinancialnewsnotebook

logistic-regression machine-learning natural-language-processing scikit-learn tfidf-vectorizer

Last synced: about 19 hours ago
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Building the model of a financial news sentiment classifier. Financial news headlines will be classified as positive, negative or neutral (from an investor point of view)

• Host: GitHub
• URL: https://github.com/chengetanaim/sentimentanalysisforfinancialnewsnotebook
• Owner: Chengetanaim
• Created: 2023-08-19T08:40:08.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2023-08-19T08:54:30.000Z (about 1 year ago)
• Last Synced: 2024-01-27T18:42:06.337Z (9 months ago)
• Topics: logistic-regression, machine-learning, natural-language-processing, scikit-learn, tfidf-vectorizer
• Language: Jupyter Notebook
• Homepage:
• Size: 567 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SentimentAnalysisForFinancialNewsNotebook

Building the model of a financial news sentiment classifier.  Financial news headlines will be classified as positive, negative or neutral (from an investor point of view)

Open the SentimentAnalysisForFinancialNews.ipynb file. The file is where the code is but it's also a tutorial where I take you in the data science journey of data wrangling, feature engineering, eda and model building.

You can open the file right here in your browser.


I also created a web application for this model using Django, the code for this can be found here - https://github.com/Chengetanaim/SentimentAnalysisForFinancialNews