AI Deep Learning with Multiple Labels for Sentiment Classification of Tweets

Abstract

We introduce an incremental transfer learning pipeline for AI systems for ordinal classification based on multiple labels of Tweets. In this pipeline, 5 sub-models are trained and the incremental knowledge is transferred to achieve higher model complexity and better performance. Each training example has multiple labels, with a target label for each sub-model. The first sub-model is a polarity classification model for negative, neutral, and positive sentiments. The second and third sub-models are ordinal classification models for positive and negative sentiments. The fourth sub-model is a binary classification model for neutral sentiment. The last sub-model is a seven-class model for polarity and intensity classes. The proposed method is applied on the Semantic Evaluation 2018 Task 1 Affects in Tweets Subtask V-oc (ordinal classification task). We experiment with 5 word embeddings, and apply ensemble methods to combine their outputs to boost overall performance. We use weighted average and stacking technique on the proposed systems and the DeepMoji model which is retrained for transfer learning. We achieve a Pearson correlation coefficient of 0.806 on the test data of SemEval-2018, which would have ranked the 4th in the SemEval-2018 Task 1 Subtask V-oc.