IBNN: an integrated BERT-neural network framework for sentiment and emotion based text analysis

Social networks are experiencing an unprecedented surge in demand for text mining applications. Text analysis, particularly on online platforms, has become increasingly prevalent. With the rapid growth of social media, enormous volumes of text data, including user comments, are now readily accessible. Analyzing these comments is highly valuable for various business applications. Sentiment Analysis (SA), a natural language processing technique (NLP), is employed to detect emotions and sentiments expressed in text. This paper proposes the development of the IBNN (Integrated BERT with Neural Network) model, which incorporates various feature extraction techniques such as CountVectorizer, TF-IDF Vectorizer, Word2Vec, GloVe, and Bidirectional Encoder Representations from Transformers (BERT) to predict individuals' emotional states through SA. The primary objectives of this work are twofold. First, it compares the effectiveness of these five feature extraction techniques using machine learning, ensemble, and neural network algorithms such as Decision Tree, Random Forest, XGBoost, Neural Network, and the proposed IBNN-across four emotion datasets. Second, it demonstrates the efficacy of the proposed IBNN model, which integrates BERT with neural networks, in achieving highest results across four datasets.