Man Liu


2018

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EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets
Man Liu
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This 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.

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EmoNLP at SemEval-2018 Task 2: English Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional LSTM
Man Liu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation.
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