Twitter Sentiment Analysis #582
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Our team will soon review your PR. Thanks @JagritiGautam793 :) |
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Please fix it up using the REAME template, https://github.com/abhisheks008/ML-Crate/blob/main/.github/readme_template.md |
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ML-Crate Repository (Proposing new issue)
Project Title :Twitter sentiment analysis
Aim :to predict whether a comment is positive or negative on twitter.
Dataset:https://www.kaggle.com/datasets/kazanova/sentiment140
This project comprises of the comparison between the Vader and RoBERTA model on tweets sentimental analysis .In summary, VADER is quick and fairly robust but less accurate than RoBERTa overall. RoBERTa requires more effort with fine-tuning but can achieve state-of-the-art accuracy for Twitter sentiment if properly trained. The choice depends on the use case's requirements for speed, customization, and accuracy.In last the transformer pipelines is also applied to match on the speed and accuracy of the tweets analysis ...Roberta and transformer pipelines are predicting the tweets in more accurate way on the basis of human context ..