Description
Assignment #7
Twitter US Airline
Text Sentiment Classification
Overview
- Sentiment classification is the automated process of identifying opinions in text and labeling them as positive, negative, or neutral, based on the emotions customers express within them.
- In this assignment, you need to train a recurrent neural network (RNN) or fine-tune a pre-trained language model (e.g., BERT) to predict the sentiment of given tweet.
- You can use pre-trained model.
Dataset
- Twitter US Airline Sentiment from kaggle
- Twitter data was scraped from February of 2015 about each major
U.S. airline
- Contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons.
- This assignment dataset link
- We resample the data and split it into three groups: train, val and test
- Replace sentiment by (positive, 2) (neutral, 1) (negative, 0)
Your task
• Skeleton code: https://colab.research.google.com/drive/1i6bqF82EbMY7dnLYuPWM_o0D cF2ceuLx
- Using word embedding to represent the word
• You can use torch.nn.Embedding to learn word embeddings• Example: LSTM for part-of-speech tagging
• Or use pre-trained GloVe or fastText word embeddings for better performance • Example: torchtext, Deep Learning For NLP with PyTorch and Torchtext
• Notice : You need use all text (train, val, test) to get word embeddings - Using a pre-trained model of your choice, you are to build a deep network that predicts the sentiment of a given tweet.
• PyTorch-transformers pre-trained models
Your task (cont.)
• Output is three sentiment polarity • Positive: 2
• Neutral: 1 • Negative: 0
• Submission format:
• Follow the index number in test.csv
Things you cannot do
- You cannot submit results predicted by others.
- You cannot copy trained models from others.
- You cannot copy code from others, internet, GitHub …
- You cannot collect more images to train your model in order to boost performance.
Any violation will result in 0 score!
