Deep Emotion Recognition in Text (PAST)

April 17, 2018
Duration: One Semester
Goals: Build an emotion recognizer for text using deep learning methods
Supervisor: Dr. Pearl Pu
Student Name: Open
Keywords: Emotion recognition, classification, deep learning, machine learning

This project aims at analyzing the emotions presented in some given text using deep learning approaches. This is usually cast as a multi-class (or multi-label) classification problem, where a given piece of text (for example a tweet, a review for some movie, or a piece of news) is classified into one (or several) emotion category (categories). Current deep learning methods for the task of text classification include convolutional neural networks, recurrent neural networks, or hybrid models.

During the project, the student is supposed to:

  • understand various emotion models;
  • understand word embeddings;
  • understand some of the state-of-the-art deep learning methods for text classification;
  • devise a neural network structure;
  • find some suitable dataset(s) for training and evaluation;
  • compare the deep learning method with traditional approaches.
Related Skills: Background or interest in machine learning, deep learning, and natural language processing
Suitable for: Master Student. Interested student should contact Dr. Pu along with a copy of your CV.