Analyzing Sentiments using Optimized Novel Ensemble Fuzzy and DL based Approach with Efficient Feature Selection and Extraction Models

Abstract

The most popular and active area of data mining study is sentiment analysis. Twitter is a crucial platform for collecting and distributing people’s thoughts, feelings, views, and attitudes regarding specific entities. There are several social media networks available today. In light of this, sentiment analysis in the Natural Language Processing (NLP) field became fascinating. Various methods for analyzing sentiment have been explored. However, improvements are still required regarding reliability and system efficacy. Additionally, user emotional expressions typically take the form of naturally occurring human-written textual data with numerous noises and ambiguities. The intricate contextual significance of sentiment expressions is difficult for present studies on sentiment analysis to precisely capture and interpret, particularly in linguistics with complex frameworks. To address these issues, we presented a new integrated fuzzy neural network. The proposed framework is developed for effective and efficient feature selection and hybrid approach based sentiment analysis. Ensemble of novel Deep Convolutional Neuro-Fuzzy Inference System (DCNFIS) and Deep Learning-based (DL) Long short-term memory neural network multilayer stacked bidirectional LSTM neural network analyzes the sentiments. The provided dataset is initially cleaned up and filtered out as part of preprocessing. Utilizing the preprocessed data, sentiment-based features are extracted using the inception-ResNet-V2 model. Then, the relevant features are selected by employing the Enhanced Reptile Search Algorithm (ERSA). The Al-Biruni Earth Radius (BER) optimization algorithm is used to optimize the hyperparameters of the ensemble approach, which analyzes the sentiment categories such as positive, negative, very positive, very negative, and neutral. Finally, an effectiveness assessment of the suggested and present classifiers is presented. Using three distinct research datasets, we conducted an experimental evaluation of the suggested model. While differentiating from the proposed approach, the proposed approach yields a greater performance of 98.97% recall, 99.06% precision, 99.13% accuracy and 99.01% F1-score. The experimental investigation analyzes that the proposed approach gains superior performance over existing approaches