Feature Selection and Extraction Models

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 natural language processing (NLP) field became fascinating. Different techniques have been developed for sentiment analysis. However, there is still a need for improvement in terms of accuracy and system effectiveness. Additionally, user emotional expressions typically take the form of naturally occurring human-written textual data with numerous noises and ambiguities. To address these issues, we presented a new integrated fuzzy neural network. The proposed framework is developed for effective and efficient feature selection and deep fuzzy-based sentiment analysis. Deep Convolutional Neuro Fuzzy Inference System (DCNFIS) analyzes the sentiments. The provided dataset is initially cleaned up and filtered out as part of pre-processing. Utilizing the pre-processed data, sentiment-based features are extracted using Inception-ResNet V2 model. Then, relevant features are selected by employing Enhanced Reptile Search Algorithm (ERSA). The Al-Biruni Earth Radius (BER) Optimization Algorithm is used to optimize the DCNFIS structure, which analyzes sentiment categories such as positive, negative, very positive, very negative, and neutral. Finally, an effectiveness assessment of suggested and present classifiers is presented. The experimental investigation analyzes that the proposed approach gains superior performance over existing approaches.