DefinePK

Abstract

For many applications, such as sentiment analysis, topic modelling, and information retrieval, pattern recognition in textual data is crucial. In order to find and classify patterns in textual data, this work explores the use of machine learning techniques for in-depth textual feature analysis. Data is first acquired from a variety of sources, such as reviews, articles, and social media. Text pre processing methods including cleaning, tokenization, and lemmatization are used to get the data ready for analysis. Feature extraction methods like Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings like Word2Vec and BERT are used to convert text into numerical representations that capture semantic value. Feature selection techniques that reduce dimensionality and improve model performance, such as Chi-Square and Mutual Information, are then used to identify the most significant features. Numerous machine learning techniques are assessed for classification, including Support Vector Machines (SVM), Transformers, Random Forests, Naive Bayes, and Recurrent Neural Networks (RNNs). These algorithms are tested and trained on split datasets to ensure their robustness and dependability. The models' efficacy is assessed using performance indicators like F1-score, recall, accuracy, and precision. The proposed system is suitable for usage in real-world scenarios due to its high accuracy and scalability. This study shows how textual qualities can be evaluated and categorized using machine learning. It also demonstrates how these technologies can be applied to enhance pattern identification and interpretation in vast volumes of textual data, producing valuable insights and supporting informed decision-making across a range of industries