Developing a conceptual framework for short text categorization using hybrid CNN- LSTM based Caledonian crow optimization

Abstract

Due to the continuous progression of social media networking sites, people share their thoughts, viewpoints, videos, speech and images through short texts. But the short texts are manually understandable but hard for the machine to collect data for clarification. The limited terms present in the short texts seem difficult while categorizing, analyzing as well as evaluating. Since social media and Bibliographic repositories like Digital Bibliography and Library Project (DBLP) contains a substantial amount of information, it is necessary to mine only useful information from the existing short texts. To achieve effectiveness and efficiency in short text categorization, the content-related characteristics derived from various machine learning techniques are admired. The significant objective of the proposed approach involves the categorization of short text and enhancing its accuracy, which can improve further finding collaborative research communities.. In this work, a hybrid convolutional neural network-long short term memory (CNN-LSTM) based new Caledonian crow optimization (NC2LO) model is utilized to classify the short texts. The crows utilize both social and asocial learning for developing tool modelling skills. Getting attracted to the behaviour of this variety of crows, and motivated by the learning strategy of crows, a new Caledonian crow optimization model referred to as NC2LO model is established. The conceptual framework of the proposed methodology consists of four different phases namely the data collection phase, data preparation phase, data pre-processing phase and short text categorization phase to classify the short texts with a high accuracy rate. Then, the proposed CNN-LSTM based NC2LO for short text categorization is evaluated using four different types of datasets namely IMDb, AG news, Twitter, and Tagmy News. Finally, the comparative analysis is carried out to evaluate the accuracy rate for various approaches and the analysis demonstrated that the proposed approach achieves a high accuracy rate of about 97%.