Using tweets from Twitter, I look at exploring sentiment analysis models, using both a pre-trained VADER model (available via NLTK) and a basic bag-of-words classifier. The material is meant for self-study/EDA. Some other links of interest:
- https://www.kaggle.com/kazanova/sentiment140
- https://www.nltk.org/data.html
- https://medium.com/@randerson112358/stock-market-sentiment-analysis-using-python-machine-learning-5b644f151a3e
- https://regex101.com/
- https://algotrading101.com/learn/sentiment-analysis-python-guide/
- https://www.kdnuggets.com/2016/06/politics-analytics-trump-clinton-sanders-twitter-sentiment.html
- https://amiham-singh.github.io/
- https://icwsm.org/papers/3--Godbole-Srinivasaiah-Skiena.pdf
- https://scikit-learn.org/stable/modules/feature_extraction.html