SemEval-2017 Task 4 Sentiment Analysis in Twitter

Introduction

SemEval-2017 Task 4 is a text sentiment classification task: Given a message, classify whether the message is of positive, negative, or neutral sentiment.

Run Experiments

# install the environment
conda create -n allennlp python=3.6
source activate allennlp
pip install -r requirements.txt

# run the program
chmod +x run.sh  && ./run.sh

Experiments Results

In the table, the first two models are the SOTA models for this task (ensemble and single)(as far as I know). I use AllenNLP to implement the other models below.

I found that GloVe is pretty for single embedding compared to the other embeddings. The model GloVe + Bi-LSTM + attention is similar to the model Deep Bi-LSTM+attention, and the results are similar. The model GloVe + ELMo + BCN achieves 0.682 F1 score with single model which is also compatible to the model LSTMs+CNNs ensemble with multiple conv. ops.

Models	F1-score
LSTMs+CNNs ensemble with multiple conv. ops (Ensemble)	0.685
Deep Bi-LSTM+attention (Single model)	0.677
GloVe(No Grad) + 2 BiLSTM	0.629
GloVe(With Grad) + 2 BiLSTM	0.642
ELMo(No Grad) + 2 BiLSTM	0.614
ELMo(With Grad) + 2 BiLSTM	0.609
BERT(No Grad) + 2 BiLSTM	0.599
BERT(With Grad) + 2 BiLSTM	0.480
GloVe + Bi-LSTM + attention	0.665
GloVe + ELMo + BCN	0.682

Reference

1. Rosenthal S, Farra N, Nakov P. SemEval-2017 task 4: Sentiment analysis in Twitter[C]//Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). 2017: 502-518.
2. Baziotis C, Pelekis N, Doulkeridis C. Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis[C]//Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). 2017: 747-754.
3. Cliche M. BB_twtr at SemEval-2017 task 4: twitter sentiment analysis with CNNs and LSTMs[J]. arXiv preprint arXiv:1704.06125, 2017.