An Optimized Crossover Framework for Social Media Sentiment Analysis

Abstract

Using deep learning, a new sentiment analysis model is designed in our article. The original data (input) is first pre-processed by stemming, stop-word removal, and tokenization. The projected technique includes 4 phases: "pre-processing, feature extraction, feature selection, and sentiment classification." The characteristics “Bigram-BoW (B-BoW), Threshold Term Frequency-Inverse Document Frequency (T-TFIDF), Unigram, and N-Gram” are then retrieved from the pre-processed data. Utilizing the self-improved Honey Badger Algorithm, the best features out of the chosen features will be chosen (SI-HBA). The basic Honey Badger Algorithm (HBA) has been conceptually improved by this SI-HBA model. The review classification will then be conducted via the proposed optimized crossover framework, which is constructed by hybridizing the optimized Bi-Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) trained with Transfer learning, respectively. The SI-HBA model’s optimally chosen features are used to train the hybrid classifier within the optimized crossover architecture. A self-improved Honey Badger Algorithm is used to fine-tune the weight of the Bi-LSTM classifier to improve the classification performance of the gathered reviews (SI-HBA). The final results will indicate whether the reviews are mostly good, negative, or neutral. The proposed sentiment classification model is then validated by a comparison analysis.