Natural Language Processing for Literacy Text Mining: Extracting Knowledge From British National Corpus

The text-mining process has been transformed through Natural Language Processing (NLP), it allows researchers to extract essential information from extensive textual resources. The research investigates the implementation of NLP approaches for literary text mining through the British National Corpus (BNC) which gathers an extensive collection of English literature texts spanning multiple genres. A research analysis of linguistic patterns along with authorial styles and thematic structures inside the corpus depends on modern text mining methodologies. This method includes three key stages with data preprocessing steps which include tokenization, stopword removal and lemmatization that proceed the feature extraction stage that utilizes TF-IDF and word embeddings followed by linguistic analysis that utilizes NER and POS tagging. LDA alongside BERT form part of the topic modeling methods used to discover hidden patterns within literary documents. Text analysis and stylistic evaluation provide additional information about the emotion-based properties and structural aspects that exist within the texts. The study uses Support Vector Machines (SVM) among machine learning classifiers to perform text classification along with thematic clustering. The research study applies visualization methods such as word clouds and dependency parsing and network graphs for displaying literary structures with relationship representation. The research indicates NLP's capability to improve literary text evaluation because it shows language patterns alongside generic features as well as narrative style adaptations. The research method establishes pathways for computational literature studies to extract organized knowledge from extensive text databases also known as BNC. It merges computational linguistics with scholarly studies in literature.