Comparative Analysis on Classification of Unstructured Text data and Specific Summarization
S. B. Harisha, Subrhamanya Bhat · 2024
Unstructured text data, prevalent in emails, social media posts, articles, and more, poses unique challenges and opportunities for classification. This abstract delves into methods and applications of classifying unstructured text data. Classification techniques for unstructured text data leverage natural language processing (NLP), machine learning, and deep learning. NLP techniques, including tokenization, stemming, and part-of-speech tagging, preprocess raw text to extract meaningful features. Traditional machine learning algorithms such as Naive Bayes, Support Vector Machines, and Random Forests, alongside deep learning architectures like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), are employed for classification tasks. Topic modeling techniques categorize text data into topics or themes, facilitating content organization and recommendation systems. Despite the advancements, challenges persist in unstructured text data classification. Ambiguity, context dependency, and linguistic nuances pose hurdles in accurately categorizing text data. Domain-specific jargon and slang further complicate classification tasks, necessitating domain adaptation and continuous model refinement. In conclusion, classification of unstructured text data is a multidimensional endeavor, requiring interdisciplinary approaches and continual innovation to effectively derive insights and value from vast textual information sources.