Abrar2652/nlp-roBERTa-biLSTM-attention
An NLP research project utilizing the "cardiffnlp/twitter-roberta-base-sentiment-latest" pre-trained transformer for tweet tokenization. The project includes an attention-based biLSTM model that predicts sentiment labels for tweets as negative (-1), neutral (0), or positive (1).
This project offers a method for automatically analyzing the emotional tone of tweets related to COVID-19. It takes raw or cleaned social media text about the pandemic and classifies each tweet as negative, neutral, or positive. This is especially useful for public health researchers or social scientists tracking public sentiment during health crises.
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Use this if you need to quickly understand widespread public emotional responses to specific events, especially those discussed on Twitter, without manually reading thousands of posts.
Not ideal if your primary goal is to analyze sentiment from very diverse text sources beyond social media, or if you need highly nuanced sentiment categories beyond simple positive, neutral, or negative.
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License
MIT
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Last pushed
Oct 06, 2024
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