A Novel Hybrid Two-Pass Optimized Deep Neural Network Classifier (Cnn-Olstm) For Twitter Sentiment Analysis

Abstract

The goal of sentiment analysis is to identify and categorize the emotions expressed in tweets, messages, and user reviews.Social media platforms like Instagram, Facebook, and Twitter generate a lot of emotionally charged data, which may be quite beneficial when trying to improve the quality of both products and services uniformly.Even though various machine learning algorithms have been designed to recognize the emotions of Twitter users, sentiment analysis still faces significant obstacles.A two-pass optimized deep neural network has been designed to address this.The long short-term memory (LSTM) classifier and the convolution neural network (CNN) are used to create the proposed hybrid two-pass classifier (CNN-OLSTM).In addition, the Adaptive sunflower optimization(ASFO)technique is proposed to optimize the parameter existing in a two-pass classifier to improve its performance.Various measures are used to evaluate the effectiveness of the proposed strategy.