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

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