Using machine and deep learning techniques, this research explores an efficient approach for categorizing textual data. Just like a master chef who carefully selects and prepares ingredients for a delicious dish, the researchers clean the data, handle missing values, and remove unnecessary columns. They then utilize various algorithms such as logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms like long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) to classify the text. Their results are impressive – the LSTM model achieves an incredible 92% accuracy, outperforming all other models and baseline studies. Imagine having a powerful tool that effortlessly sorts through piles of texts like a librarian with superhuman speed and accuracy! This research opens the doors to a new era of efficient text classification using deep learning techniques. To learn more about their methodology and findings, explore the underlying research!

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

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