https://twitter.com/MusaHakeem010/status/1379017208593068032

https://twitter.com/MusaHakeem010/status/1379017208593068032

Grant Hadlich

@GrantHadlich

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 272 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (69.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (57.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

Each week I pull ~51000 tweets on US State mentions and do sentiment analysis. Most positive state was #Maine 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 #Maine!
#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 #DistrictofColumbia!
#NLP #Python #ML

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 528 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

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 569 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

I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (53.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

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

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

Read 7 tweets

Grant Hadlich

@GrantHadlich

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

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

Read 7 tweets

Grant Hadlich

@GrantHadlich

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 #VADER model from #NLTK.
Which state had the most positive mentions this week? It was #Nevada!
#NLP #Python #ML

Read 7 tweets

Grant Hadlich

@GrantHadlich

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

Grant Hadlich

@GrantHadlich

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

Read 7 tweets

Grant Hadlich

@GrantHadlich

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

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

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 600 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (64.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 600 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.7%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Musa Hakeem 🇳🇱

@MusaHakeem010

1/ #Jezus—God, Zoon van God of profeet?

Als we de christelijke Bijbel lezen, zien we dat 'Zoon van God' niet letterlijk te nemen is.

Wij zijn hopelijk allemaal #Gods #kinderen, echter vermijden wij zulk taalgebruik omdat #christenen het verkeerd hebben begrepen.

https://twitter.com/MusaHakeem010/status/1379017208593068032

2/ De Bijbel is duidelijk dat 'kind van God zijn' spreekwoordelijk is. Het betekent dat je een goed mens bent.

Matteüs 5
9 Zalig zijn de vredestichters, want zij zullen Gods kinderen genoemd worden.

En #Jezus behoort tot de beste der mensen, en zijn moeder was een kuise vrouw.

3/ Als Jezus dan niet de letterlijke Zoon van God is, kan hij God zelf zijn?

Nee, Jezus verwerpt God te zijn. En de Bijbel geeft dat te kennen in vele, vele passages.

Read 8 tweets

Musa Hakeem 🇳🇱

@MusaHakeem010

/1 "Hoe verklaar je de #Drieeenheid?"—een samenvatting van mijn #dialoog met een #christen.

Iemand was zo lief om voor mij te bidden, waarop ik zei dat ik dat waardeer; ik zal ook voor die persoon bidden, "maar als je bidt, bid dan naar de #Vader; de Enige Waarachtige #God".

2/ —een verwijzing naar de #Bijbel:

Johannes 17
1 Dit sprak Jezus, en Hij sloeg Zijn ogen op naar de hemel en zei: #Vader [...]
3 En dit is het eeuwige leven, dat zij U kennen, de #enige #waarachtige #God, en Jezus Christus, Die U gezonden hebt.

3/ Als Jezus zegt dat niet hij, maar de Vader de "enige waarachtige God" is, dan betekent het dus dat Jezus niet zelf God is.

#Jezus #Christus (vrede zij met hem) was altijd nederig en erkende altijd dat er iemand boven hem was: God. Nooit eerde en prees hij zichzelf.

Read 13 tweets

Musa Hakeem 🇳🇱

@MusaHakeem010

https://twitter.com/geertwilderspvv/status/1357772553280962567

/1 Ten eerste, dat is niet wat in de Koran staat. Het woord "minderwaardig" is Wilders' geforceerde interpretatie op de vers.

Skeptici zouden zich af moeten vragen waarom dhr. Wilders altijd met zulke valse aantijgingen komt. Waarom de waarheid verdraaien als je "gelijk" hebt?

https://twitter.com/geertwilderspvv/status/1357772553280962567

2/ De Koran zegt het volgende (vertaling):

Soera 33, Al-ʾAḥzāb
35 Voorwaar, de mannen die zich hebben overgegeven (aan Allāh) en de #vrouwen die zich hebben overgegeven, en de gelovige mannen en de gelovige #vrouwen, en de gehoorzame mannen en de gehoorzame #vrouwen, [...]

3/ [...] en de waarachtige mannen en de waarachtige #vrouwen, en de geduldige mannen en de geduldige #vrouwen, en de ootmoedige mannen en de ootmoedige #vrouwen, en de bijdragen gevende mannen en de bijdragen gevende #vrouwen, en de vastende mannen en de vastende #vrouwen [...]

Read 15 tweets

Fro VR Edition

@FrostisAdvance

@Factornews

[Thread] Comme j'ai un #OculusRiftS depuis fin mai 2019. Cela fait donc quelques mois et j'ai déjà testé un paquet de choses. Donc j'vais faire un petit thread (en plus des possibles tests sur @Factornews), histoire de vous donner les bons tuyaux.

Evidemment, il y aura des jeux, des expériences, des vidéos, du payant, du gratuit, du vieux, du neuf, bref, un peu de tout. De toute manière, lorsque l'on fait l'acquisition d'un casque de VR, tout est plus ou moins nouveau :)

Je viens tout juste de sortir de #WolvesInTheWalls de #FableStudio. C'est dispo par ici gratuitement (oculus.com/experiences/ri…) et il faut compter une petite heure pour terminer l'histoire, inspirée d'une nouvelle de #NeilGaiman. C'est vraiment chouette !

Read 63 tweets

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