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This project was completed as part of the "Twitter Sentiment Extraction" Kaggle competition in June 2020 (https://www.kaggle.com/c/tweet-sentiment-extraction). This project involved extracting the text related to emotions found in Tweets. Our team submitted this git hub project to Kaggle, we scored in top 1000 :)
We hope that this project will help you get a jump start on sentiment extraction!
Our code is largely based on the simple transformer work done by: xxx here http://google.com
Sentiment extraction takes sentiment analysis to the next level. In legacy NLP tasks it was enough to predict the emotion of a certain tweet, for example:
| Tweet | Predicted Sentiment |
|---|---|
| col 3 is | right-aligned |
However sentiment extraction takes this NLP to the NEXT LEVEL by extracting the words that contribute to the reason behind a particular sentiment. For example, see the example below:
| Tables | Are | Cool |
|---|---|---|
| col 3 is | right-aligned | $1600 |
| col 2 is | centered | $12 |
| zebra stripes | are neat | $1 |
- NLP is important business applications such as th tweets, product reviews, customer service
- emotion analysis -> emotion extraction
- transformers
- social media data
- provide insights to business
- Advanced Techniques
- We used Huggingface pertained transformer models (Roberta-base-cased) and added customized question-answer head layers using TensorFlow to reach a better question answering result.
- Please see more details from this notebook Link to Tensorflow approach
- Key difference:
- TensorFlow approach can support more customized functions including customizing loss function, question answering structures, the number of inputs, and doing k-fold.
- TensorFlow approach can help you understand deeper on how transformers work by creating attention mask, input ids, and padding your train set.
- The training time takes longer for this approach as we are using the k-fold method to avoid overfitting
- Key Insights
- Lessons Learned and Best Practices
- Resources