I analyzed the sentiment on the last 236 tweets from my home feed using a pretrained TextBlob model. A majority (53.0%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/Ed0794A0gu textblobpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 236 tweets from my home feed using a NaiveBayes model from NLTK. A majority (61.4%) were classified as positive. Python NLP Classification Sentiment GrantBot https://t.co/Olivyl9Gbi naivebayesnltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 236 tweets from my home feed using a pretrained VADER model from NLTK. A majority (53.4%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/5oL2UQ6Bvf vadernltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 236 tweets from my home feed using a pretrained BERT model from huggingface. A majority (65.3%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/RRrkpQGCUn berthuggingfacepythonnlpclassificationsentimentgrantbot
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I analyzed the sentiment on the last 413 tweets from my home feed using a pretrained TextBlob model. A majority (50.1%) were classified as positive. Python NLP Classification Sentiment GrantBot https://t.co/jyjOqkbOgX textblobpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 413 tweets from my home feed using a NaiveBayes model from NLTK. A majority (60.8%) were classified as positive. Python NLP Classification Sentiment GrantBot https://t.co/V6NCgCcZqE naivebayesnltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 413 tweets from my home feed using a pretrained VADER model from NLTK. A majority (54.7%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/lnuLkUz2DL vadernltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 413 tweets from my home feed using a pretrained BERT model from huggingface. A majority (65.4%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/yOPdH3QWEI berthuggingfacepythonnlpclassificationsentimentgrantbot
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I analyzed the sentiment on the last 194 tweets from my home feed using a pretrained TextBlob model. A majority (51.0%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/GboD3hwMud textblobpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 194 tweets from my home feed using a NaiveBayes model from NLTK. A majority (63.4%) were classified as positive. Python NLP Classification Sentiment GrantBot https://t.co/6iwGlyvtT7 naivebayesnltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 194 tweets from my home feed using a pretrained VADER model from NLTK. A majority (58.8%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/hdODPSRNvq vadernltkpythonnlpclassificationsentimentgrantbot
I analyzed the sentiment on the last 194 tweets from my home feed using a pretrained BERT model from huggingface. A majority (67.0%) were classified as negative. Python NLP Classification Sentiment GrantBot https://t.co/F6Q4IBA7my berthuggingfacepythonnlpclassificationsentimentgrantbot
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