UNLOCKING THE POWER OF NATURAL LANGUAGE PROCESSING (NLP) FOR TEXT ANALYSIS
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Abstract
Abstract. Natural Language Processing (NLP) is a rapidly growing field that revolutionizes the way we analyze and understand textual data. In this article, we delve into the transformative power of NLP, exploring the process of converting raw text data into a corpus of documents, effective methods for representing text, essential transformations to enhance data quality, summarization techniques using TF-IDF, and visualizations that unveil word frequencies. By leveraging NLP, we unlock a wealth of insights, patterns, and actionable information from vast amounts of text, enabling us to make informed decisions and extract meaningful knowledge from the ever-expanding world of language.
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References
Jurafsky, D., & Martin, J. H. (2019). Speech and Language Processing (3rd ed.). Pearson Education.
Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.
Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 4171-4186.
Witten, I. H., Frank, E., & Hall, M. A. (2016). Data Mining: Practical Machine Learning Tools and Techniques (4th ed.). Morgan Kaufmann.