An innovative approach to classify hierarchical remarks with multi-class using BERT and customized naïve bayes classifier

Abstract

Text classification is the process of grouping text into distinct categories. Text classifiers may automatically assess text input and allocate a set of pre-defined tags or categories depending on its content or a pre-trained model using Natural Language Processing (NLP), which actually is a subset of Machine Learning (ML). The notion of text categorization is becoming increasingly essential in enterprises since it helps firms to get ideas from facts and automate company operations, lowering manual labor and expenses. Linguistic Detectors (the technique of determining the language of a given document), Sentiment Analysis (the process of identifying whether a text is favorable or unfavorable about a given subject), Topic Detection (determining the theme or topic of a group of texts), and so on are common applications of text classification in industry. The nature of the dataset is Multi-class and multi-hierarchical, which means that the hierarchies are in multiple levels, each level of hierarchy is multiple class in nature. One of ML’s most successful paradigms is supervised learning from which one can build a generalization model. Hence, a custom model is built, so that the model fits with the problem. Deep learning (DL), part of Artificial Intelligence (AI) , does functions that replicate the human brain's data processing capabilities in order to identify text or artifacts, translate languages, detect voice, draw conclusions and so on. Bidirectional Encoder Representations from Transformers (BERT), a Deep Learning Algorithm performs an extra-ordinary task in NLP text classification and results in high accuracy. Therefore, BERT is combined with the Custom Model developed and compared with the native algorithm to ensure the increase in accuracy rates.