A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods

Due to the rapid incline in the number of documents along with social media usage, text categorization has become an important concept. There are tasks required to be fulfilled during the text categorization, such as extracting useful data from different perspectives, reducing the high feature space dimension, and improving effectiveness. In order to accomplish these tasks, feature selection, and feature extraction gain importance. This paper investigates how to solve feature selection and extraction problems. Also, this study aims to decide which topics are the focus of a document. Moreover, the Twitter data-set is utilized as a document and an Uncapacitated P-Median Problem (UPMP) is applied to make clustering. In this study, UPMP is used on Twitter data collection for the first time to collect clustered tweets. Therefore, a novel hybrid genetic bat algorithm (HGBA) is proposed to solve the UPMP for our case. The proposed novel approach is applied to analyze the Twitter data-set of the Nepal earthquake. The first part of the analysis includes the data pre-processing stage. The Latent Dirichlet Allocation (LDA) method is applied to the pre-processed text. After that, a similarity (distance) matrix is generated by utilizing the Jensen Shannon Divergence (JSD) model. The study's main goal is to use Twitter to assess the needs of victims during and after a disaster. To evaluate the applicability of the proposed approach, experiments are conducted on the OR-Library data-set. The results demonstrate that the proposed approach successfully extracts topics and categorizes text.