Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)

This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English.

• Reference Paper: TimeLMs paper.
• Git Repo: TimeLMs official repository.

Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive

This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.

Example Pipeline

from transformers import pipeline
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("Covid cases are increasing fast!")

[{'label': 'Negative', 'score': 0.7236}]

Full classification example

from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Preprocess text (username and link placeholders)
def preprocess(text):
 new_text = []
 for t in text.split(" "):
 t = '@user' if t.startswith('@') and len(t) > 1 else t
 t = 'http' if t.startswith('http') else t
 new_text.append(t)
 return " ".join(new_text)
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
#model.save_pretrained(MODEL)
text = "Covid cases are increasing fast!"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Covid cases are increasing fast!"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
# Print labels and scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
 l = config.id2label[ranking[i]]
 s = scores[ranking[i]]
 print(f"{i+1}) {l} {np.round(float(s), 4)}")

Output:

1) Negative 0.7236
2) Neutral 0.2287
3) Positive 0.0477

References

@inproceedings{camacho-collados-etal-2022-tweetnlp,
 title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
 author = "Camacho-collados, Jose and
 Rezaee, Kiamehr and
 Riahi, Talayeh and
 Ushio, Asahi and
 Loureiro, Daniel and
 Antypas, Dimosthenis and
 Boisson, Joanne and
 Espinosa Anke, Luis and
 Liu, Fangyu and
 Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
 booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
 month = dec,
 year = "2022",
 address = "Abu Dhabi, UAE",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2022.emnlp-demos.5",
 pages = "38--49"
}

@inproceedings{loureiro-etal-2022-timelms,
 title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
 author = "Loureiro, Daniel and
 Barbieri, Francesco and
 Neves, Leonardo and
 Espinosa Anke, Luis and
 Camacho-collados, Jose",
 booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
 month = may,
 year = "2022",
 address = "Dublin, Ireland",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2022.acl-demo.25",
 doi = "10.18653/v1/2022.acl-demo.25",
 pages = "251--260"
}