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2018 EMNLP EMNLP 2018

EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets

Abstract

AbstractThis paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three different approaches: feed forward neural network (FFNN), convolutional BLSTM (ConBLSTM) and Gradient Boosting Regression Tree Method (GBM). Word embeddings used in convolutional BLSTM are pre-trained on 470 million tweets which are filtered using the emotional words and emojis. In addition, broad sets of features (i.e. syntactic features, lexicon features, cluster features) are adopted to train GBM and FFNN. The three approaches are finally ensembled by the weighted average of predicted probabilities of each emotion label.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — implicit emotion prediction
🐣 Hot Topic Early Bird — gradient boosting
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Man Liu

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks Natural Language Processing > Applications > Sentiment Analysis Machine Learning > Learning Types > Ensemble Learning Machine Learning > Learning Types > Ensemble Methods Deep Learning > Learning Types > Deep Learning

Keywords

ensemble learning gradient boosting convolutional neural network long short-term memory neural network ensemble word embedding bidirectional lstm emotion classification tweet classification implicit emotion prediction convolutional blstm
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