Sentiment Analysis With Ensemble Hybrid Deep Learning Model

Abstract

The rapid development of mobile technologies has made social media a vital platform for people to express their feelings and opinions. Understanding the public opinions can be beneficial for business and political entities in making strategic decisions. In light of this, sentiment analysis plays an important role to understand the polarity of the public opinions. This paper presents an ensemble hybrid deep learning model for sentiment analysis. The proposed ensemble model comprises three hybrid deep learning models which are the combination of Robustly optimized Bidirectional Encoder Representations from Transformers approach (RoBERTa), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU). In the hybrid deep learning model, RoBERTa is responsible for projecting the textual input sequence into a representative embedding space. Thereafter, the LSTM, BiLSTM and GRU capture the long-range dependencies in the embedding given the class. The predictions by the hybrid deep learning model are then amalgamated by averaging ensemble and majority voting, further improving the overall performance in sentiment analysis. In addition to that, the data augmentation with GloVe pre-trained word embedding has also been applied to alleviate the imbalanced dataset problems. The experimental results show that the proposed ensemble hybrid deep learning model outshines the state-of-the-art methods with the accuracy of 94.9%, 91.77%, and 89.81% on IMDb, Twitter US Airline Sentiment dataset and Sentiment140 dataset, respectively.

Keywords

• Sentiment analysis
• transformers
• RoBERTa
• LSTM
• BiLSTM
• GRU