Abstract
Opinion mining is more challenging than it was before because of all the user-generated material on social media. People use Twitter (X) to gather opinions on products, advancements, and laws. Sentiment Analysis (SA) examines people's thoughts, feelings, and views on numerous topics. Tweets can be analyzed to determine public opinion on news, regulations, society, and personalities. The existing SA system has poor prediction performance and needs improvements for instantaneous commercial applications. The insufficient data and complexity of model configuration, which make deep learning (DL) difficult, are the main causes of low accuracy and prediction rates. Convolutional Neural Long Short-Term Memory (OTCNLSTM) optimal tiered blocks with classification learning are proposed in this research to recognize emotions. The objective is to classify tweets as happy or sad. The TCNLSTM model consists of four training blocks for local features. These blocks are designed to extract local emotions hierarchically. The Boosted Killer Whale Predation (BKWOP) strategy is implemented to find the appropriate hyperparameter and its solution set and build a stable neural network model. This research reviews textual emotional classification experiments utilizing various sentiment models and methods. To further analyze this research, a comparative experimental study using the Kaggle Twitter dataset is conducted. The results indicate that the OTCNLSTM model had superior performance compared to the other models.