How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python
GitHub: github.com/ghadlich/Daily…
#NLP #Python
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (62.8%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a #NaiveBayes model from #NLTK. A majority (56.2%) were classified as positive.
#Python #NLP #Classification #Sentiment #GrantBot
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #Flair model from #ZalandoSE. A majority (52.4%) were classified as positive.
#Python #NLP #PyTorch #Sentiment #GrantBot
#Python #NLP #PyTorch #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a trained #DistilBert model from #huggingface. There were an equal amount of positive and negative tweets.
#Python #NLP #PyTorch #Sentiment #GrantBot
#Python #NLP #PyTorch #Sentiment #GrantBot
I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #TextBlob model. A majority (52.1%) were classified as positive.
#Python #NLP #Classification #Sentiment #GrantBot
#Python #NLP #Classification #Sentiment #GrantBot
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