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  Scopus ID: 21100926589

Nanoscale Text Classification with Bi-LSTM: Enhancing Precision

Shaik Mohammed Basha, Anusha Balemla, Saniya Anjum and A. Ramesh


Vast volumes of data are produced from numerous sources, including websites and social media. It is necessary to extract significant information from text data, categorize it, and forecast end-user behavior or emotions. Natural-language processing research on text categorization is an example of a topic that organizes unstructured text input into useful category classifications. The Bi-Directional Long Short-Term Memory (Bi-LSTM) model, which has recently been used to a number of natural language processing (NLP) applications, is being employed in this study to classify sentences. LSTM models are more suited for text classification because they can capture long-term dependencies between word sequences. Our suggested approach performs better than the current Machine Learning models.

Published on: December 01, 2023
doi: 10.17756/nwj.2023-s4-070
Citation: Basha SM, Balemla A, Anjum S, Ramesh A. 2023. Nanoscale Text Classification with Bi-LSTM: Enhancing Precision. NanoWorld J 9(S4): S417-S420.

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