Categorization of Textual Data: Harnessing Recurrent Neural Networks and Concept Net for Extensive Topic Mining

Text analysis uses machine learning (ML) and natural language processing (NLP) to find underlying patterns in text documents. These techniques include textual data categorization and topic modeling. This unsupervised technique looks at words and phrases that appear frequently in a collection of texts and arranges them to produce a useful synopsis of the material. Deep recurrent neural networks, like Long ShortTerm Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) networks, are used in this method to classify documents. The models train the system using cosine similarities, which are computed using semantic networks such as ConceptNet. Two scenarios—one with and one without Term Frequency-Inverse Document Frequency (TF-IDF)—are used for the analysis.Testing on the AGNews and BBC News datasets produced results showing that applying TF-IDF greatly increased both models' accuracy and other validation measures. Through the combination of ConceptNet, WordNet, TF-IDF, and recurrent neural networks (LSTM and BiLSTM), this technique improves text classification with greater accuracy and efficiency than previous methods.