Sentimental analysis & Hate speech detection on English and German text collected from social media platforms using optimal feature extraction and hybrid diagonal gated recurrent neural network

Social networking platforms allow users to ask questions, exchange information, opinions, ideas, and promote companies. Social media's massive user-generated text is useful for study, translation, and analysis. Due of their quick spread and detrimental impact, offensive remarks, hate speech, and harassment must be discovered and deleted immediately. In non-English main language environments, code-mixed text makes hate speech detection difficult. Hate speech's thematic emphasis and target-oriented orientation are typically ignored in binary categorization methods. These methods also fail in code-mixing multilingual contexts. In this study, we propose an optimal feature extraction and hybrid diagonal gated recurrent neural network (FE-DGRNN) for hate speech detection and sentiment analysis on code-mixed texts in multiple languages. Our FE-DGRNN technique consists of three processes: preprocessing, improved seagull optimization (ISO) for feature extraction, and hybrid diagonal gated recurrent neural network (Hyb-DGRNN) for hate speech detection and sentiment analysis. We evaluate the performance of our proposed technique using the HASOC 2019 dataset, focusing on English and German. The results demonstrate high accuracy of 95%, 96%, and 92% for English tasks, with Precision and F-measure of 94%, 96%, and 91%. For German tasks, the accuracy is 92% and 93%, with Precision and F-measure of 90% and 91%.