Optimal feature selection and invasive weed tunicate swarm algorithm-based hierarchical attention network for text classification

Through social media platforms and the internet, world is becoming more connected, producing enormous amounts of data. Also, texts are collected from media, newspapers, user reviews products, company press releases, etc. The correctness classification mainly dependent on kind words utilised in corpus features for classification. Hence, due to increasing growth text data Internet, accurate organisation management has become a great challenge. this research, an effective Invasive Weed Tunicate Swarm Optimization-based Hierarchical Attention Network (IWTSO-based HAN) implemented achieving categorisation text. Here, mined thereby optimal acquired perform strategy. incorporation parametric each optimisation ensures proposed method increase convergence global solutions by improving effectiveness. obtained better performance with measures, like accuracy, True Positive Rate (TRP), Negative (TNR), precision, False (FNR) values 92.4%, 94.1%, 95.4%, 0.0758.