Modified wild Horse Herd Optimization based Bhattacharyya error constraint (BEC) based L2-norm Linear discriminant analysis (LDA) method for the sentiment analysis

Abstract

 People's opinions are analyzed via sentiment analysis in all fields. Reviews and tweets, among other formats, are used to express opinions. Irony, sarcasm, and other difficult-to-discern hidden meanings can occasionally be found in viewpoints. Artificial intelligence must be used to examine the sentiments as a result. We suggest a unique Bhattacharyya error constraint (BEC) based L2-norm linear discriminant analysis (LDA) because some of the earlier efforts lack optimization. There are some overfitting and class disparity issues in this. To address this, we used a brand-new method called Modified Wild Horse Herd Optimization (MHHO). The experiment is run to evaluate the performance of the suggested strategy and to compare it to other approaches already in use. We have used performance measures for comparison, and the results demonstrate that the suggested method successfully assesses the sentiment from the acquired dataset.