A Hybrid Deep Learning Framework for Efficient Sentiment Analysis

Abstract

In the era of Microblogging and the rapid growth of online platforms, an exponential rise is shown in the volume of data generated by internet users across various domains. Additionally, the creation of digital or textual data is expanding significantly. This is because consumers respond to comments made on social media platforms regarding events or products based on their personal experiences. Sentiment analysis is usually used to accomplish this kind of classification on a large scale. It is described as the process of going through all user reviews and comments that are discovered in product reviews, events, or similar sources in order to look for unstructured text comments. Our study examines how deep learning models like LSTM, GRU, CNN, and hybrid models (LSTM+CNN, LSTM+GRU, GRU+CNN) capture complex sentiment patterns in text data. Additionally, we study integrating BOW and TF-IDF as complementing features to improve model predictive power. CNN with RNNs consistently improves outcomes, demonstrating the synergy between convolutional and recurrent neural network architectures in recognizing nuanced emotion subtleties.In addition, TF-IDF typically outperforms BOW in enhancing deep learning model sentiment analysis accuracy.