In this competition, we provide a dataset which was crawled from Twitter, and we have already labeled the emotion for these tweets by some specific hashtags in the original text. There are 8 classes (or say emotions) in our dataset: anger, anticipation, disgust, fear, sadness, surprise, trust, and joy. You have to clean the data by doing some pre-processing first. Then, apply feature engineering or any other data mining technique you have or haven't learned in the Data Mining course. The final goal is to learn a model that is able to predict the emotion behind each tweet.
- Read Data
- Data Exploration: Hashtag>>Emotion
- Data Exploration: Emoji>>Emotion
- Bert Model
- Data preprocessing
- Using pre-trained words embedding (glove)
- Bert Model
- Create submition csv
- Association Rule by Result with Hashtag, Emoji and Bert Model
Python
We thank IDEA Lab for providing this dataset, as well as evanyu and thisray for making all of this possible.
https://www.kaggle.com/c/dm2021-lab2-hw2/
Data Mining, Natural Language Processing, AI