This project attempts to use a mix of traditional NLP techniques including trigrams, HNNs, and SVDs with attempts at LSTM and BERT architectures to predict and analyze sentiment projects - macroeconomic news relevance and Twitter subtext extraction.
This project attempts to use a mix of traditional NLP techniques including trigrams, HNNs, and SVDs with attempts at LSTM and BERT architectures to predict and analyze sentiment projects - macroeconomic news relevance and Twitter subtext extraction.
This project attempts to use a mix of traditional NLP techniques including trigrams, HNNs, and SVDs with attempts at LSTM and BERT architectures to predict and analyze sentiment projects - macroeconomic news relevance and Twitter subtext extraction.
This project attempts to use a mix of traditional NLP techniques including trigrams, HNNs, and SVDs with attempts at LSTM and BERT architectures to predict and analyze sentiment projects - macroeconomic news relevance and Twitter subtext extraction.
MIT License