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Text Classification Using NLP

Based on online evaluations, social media is becoming a trend for sharing thoughts, ideas, opinions, etc., which produces a tonne of unstructured data (ie. User posts). Supervised learning methods are favoured for processing those unstructured data, which aids in improved performance optimization. Due to the complexity of healthcare data, Deep Learning (DL) approaches, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models, have recently gained popularity in healthcare applications. Deep Learning (DL) techniques offer an effective and efficient model for data analysis by revealing hidden patterns and extracting valuable information from a large volume of health data, which standard analytics are unable to perform within a certain time frame. Deep Learning (DL) approaches, in particular, rely on social healthcare network pattern recognition models to produce effective outcomes. Investigating the deep learning (DL) models used to categorise the text in social media healthcare networks is the main goal of this paper's investigation. The major goal of this study is to offer insight into how to categorise text and train data by analysing and extracting the raw input and producing the output using natural language processing (NLP). Overall, the goal of this study is to improve the text classifier's performance based on efficacy to increase accuracy and text processing speed by utilising an appropriate technique in order to generate the findings that have a good chance of being successful in the future. Smart ai technologies is the best final year engineering project makers and technical supporter to student .with ieee final year project support in best prices with great services. final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are.
1.complete documentation support
2.complete working hardware/software implemented in students environment 3.classes will held accordingly.

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