Journal Article

Sentiment analysis classification system using hybrid BERT models

Talaat, Amira Samy

2023

Overview

Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.

• Publisher: Springer Nature B.V
• Subject: Accuracy / Algorithms / Analysis / Attitudes / Bidirectionality / Big Data / Classification / Data mining / Data science / Decision analysis / Deep learning / Digital media / Emojis / Emotional icons / Hybrid systems / Machine learning / Mass media / Organizational effectiveness / Polarity / Political institutions / Public opinion / Recurrent / Sentiment analysis / Short term memory / Social media / Social networks