AI Model for Text Classification Using FastText

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The purpose of text categorization, a machine learning technique, is to automatically assign tags or categories to texts. Natural language processing (NLP)- based text classifiers can quickly analyse vast volumes of text and classify it based on emotions, themes, and human intent. FastText was created by Facebook's AI Research team and is available to the public as a free library. Its primary goal is the efficient and accurate processing of big datasets in order to provide scalable remedies for the problems of text categorization and representation. Traditional machine learning techniques used in most text categorization models suffer from issues including the curse of dimensionality and subpar performance. This research offers a fastText-based AI text classification model to address the aforementioned issues. The fastText approach allows our model to create a low-dimensional, continuous, and high-quality representation of text by mining the text for relevant information through feature engineering. The experiment uses Python to define the text dataset, and the results demonstrate that our model outperforms the baseline model trained using classic ML methods in terms of accuracy, recall, and F values.