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
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (63.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a #NaiveBayes model from #NLTK. A majority (60.7%) were classified as positive.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #Flair model from #ZalandoSE. A majority (55.3%) were classified as positive.
#Python #NLP #PyTorch #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a trained #DistilBert model from #huggingface. A majority (54.9%) were classified as positive.
#Python #NLP #PyTorch #Sentiment #GrantBot
I analyzed the sentiment on the last 499 tweets from my home feed using a pretrained #TextBlob model. A majority (51.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Grant Hadlich

Grant Hadlich Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @GrantHadlich

1 Aug
Each week I pull ~51000 tweets on US State mentions. Here's a collection of word clouds from tweets pulled on 2021-07-31. Each state + DC can be found in the replies!
#Python #USA #WordCloud #TwitterData Image
Each week I pull ~51000 tweets on US State mentions. Here's the word cloud for #Alabama from tweets pulled on 2021-07-31!
#Python #WordCloud #TwitterData Image
Each week I pull ~51000 tweets on US State mentions. Here's the word cloud for #Alaska from tweets pulled on 2021-07-31!
#Python #WordCloud #TwitterData Image
Read 52 tweets
1 Aug
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 Image
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (61.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (61.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets
1 Aug
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 Image
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 Image
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 Image
Read 7 tweets
31 Jul
Each week I pull ~51000 tweets on US State mentions and do sentiment analysis. Most positive state was #Utah according to an ensemble model! In the replies are the individual models.
GitHub: github.com/ghadlich/State…
#NLP #Python #ML
I analyzed the sentiment on Twitter for each state + DC from the last week using a pretrained #BERT model from #huggingface.
Which state had the most positive mentions this week? It was #Utah!
#NLP #Python #ML
I analyzed the sentiment on Twitter for each state + DC from the last week using a pretrained #VADER model from #NLTK.
Which state had the most positive mentions this week? It was #Nevada!
#NLP #Python #ML
Read 7 tweets
31 Jul
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
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (67.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
I analyzed the sentiment on the last 412 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (59.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot
Read 7 tweets
30 Jul
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 Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (62.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
I analyzed the sentiment on the last 517 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot Image
Read 7 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

:(