Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing, language teaching, translation and speech therapy. The ever-growing Online Social Networks (OSNs) experience a vital issue to confront, i.e., hate speech. Amongst the OSN-oriented security problems, the usage of offensive language is the most important threat that is prevalently found across the Internet. Based on the group targeted, the offensive language varies in terms of adult content, hate speech, racism, cyberbullying, abuse, trolling and profanity. Amongst these, hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm, social chaos or violence. Machine Learning (ML) techniques have recently been applied to recognize hate speech-related content. The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection (GOARN-OSD)model for social media. TheGOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech. In the presented GOARN-OSD technique, the primary stage involves the data pre-processing and word embedding processes. Then, this study utilizes the Attentive Recurrent Network (ARN) model for hate speech recognition and classification. At last, the GrasshopperOptimization Algorithm (GOA) is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process. To depict the promising performance of the proposed GOARN-OSD method, a widespread experimental analysis was conducted. The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.