Dwarf Mongoose Chimp Optimization Enabled RMDL for Sentiment Categorization Using Cell Phone Data

Sentiment analysis is the process of looking through digital text to determine if the emotional tone of a text is positive, negative, or neutral. It helps companies improve their product, but a serious problem arises in classifying the polarity of certain texts with information, sentences or features to forecast their opinion. Therefore, sentiment classification should be done using new technology that classifies reviews as positive or negative so that users can make effective decisions. This research paper develops an effective model to classify sentiment using cell phone data. Initially, the Amazon phone document is passed to the BERT tokenization stage to split the acquired reviews. Then, the Aspect Term Extraction (ATE) is applied and the Term Frequency-Inverse Document Frequency (TF-IDF) is extracted as the first output. Afterward, Wordnet ontology features are extricated as the second output. Moreover, features like statistical, sarcasm linguistic, and N-gram features are extracted from BERT tokenization and considered as the third output. Finally, the sentiment is classified by subjecting the obtained three outputs to Random Multimodal Deep Learning (RMDL), which is tuned by Dwarf Mongoose Chimp Optimization (DMCO). DMCO is created by the combination of the Dwarf Mongoose Optimization (DMO) and the Chimp Optimization Algorithm (ChOA). The developed DMCO-RMDL approach attained high accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, recall, and F1-score values of 93%, 92.8%, 92.2%, 91.5%, 94.1%, and 94.8%, respectively.