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Sentiment analysis is an essential natural language processing task used to determine whether data carries positive or negative emotions. It has numerous applications, including social media monitoring, customer feedback analysis, brand reputation management, and more.
Traditional methods rely on manually crafted features and machine learning algorithms like Naive Bayes, Logistic Regression, etc., which can be time-consuming and less effective.
With recent advances in deep learning techniques, we now have access to powerful tools like transformers, enabling us to build highly accurate sentiment analysis models rapidly.
In this tutorial, you will learn how to develop a sentiment analysis model using Hugging Face’s popular open-source library, Transformers. This guide assumes no prior knowledge of transformers; however, it would help if you had some experience working with Python and basic understanding of NLP concepts. Let’s get started!
