Low-Resource Language Information Processing Using Dwarf Mongoose Optimization with Deep Learning Based Sentiment Classification

Asian and low-resource language information processing refers to the field of computational linguistics that aims to develop natural language processing (NLP) technologies for languages that have fewer available language resources or are less commonly spoken. This is an important field of study because many languages in Asia and other parts of the world are underrepresented in the field of NLP, which may limit access to information and technology for speakers of these languages. The growing volume of user-generated content on the web has made sentiment analysis (SA) a significant tool for extracting data regarding human emotional states. Twitter sentiment detectors provide a superior solution for assessing the quality of products and services compared to other conventional technologies. The detection performance and classifier accuracy of SA, which can be highly dependent on classifier methods and the quality of input features have been utilised. Deep learning (DL) methods use distinct techniques to extract data from raw data such as tweets or texts and represent them in different forms of models. Therefore, this article presents a Dwarf Mongoose Optimization with Deep Learning-Based Twitter Sentiment Classification (DMODL-TSC) technique to classify sentiments based on tweets. The presented DMODL-TSC technique leverages the concepts of natural language processing (NLP) and DL. Primarily, the raw tweets are preprocessed to transform them into a useful format. Next, the DMODL-TSC technique uses the advanced FastText word embedding technique. Moreover, the bidirectional recurrent neural network (BiRNN) method is utilized for the recognition of sentiments. Finally, the DMO technique is utilized for the optimal hyperparameter optimization of the BiRNN method, which leads to effective classification performance. The comprehensive result examination of the DMODL-TSC system was tested on three datasets, and the obtained outcomes illustrate the supremacy of the DMODL-TSC approach.