How to Perform Sentiment Analysis using BERT in Python

The Python Lab
5 min readMay 23

Sentiment analysis, also known as opinion mining, is a field within natural language processing (NLP) that focuses on the analysis and interpretation of emotions, attitudes, and opinions expressed in a given piece of text. It involves the automated process of determining whether the sentiment conveyed in the text is positive, negative, or neutral. This valuable technique has gained significant attention and importance in recent years due to its wide range of applications across various domains.

With the rapid advancements in deep learning and NLP, sentiment analysis has witnessed significant improvements in accuracy and performance. One such groundbreaking model is the Bidirectional Encoder Representations from Transformers (BERT). BERT, based on the Transformer architecture, has revolutionized NLP tasks by leveraging bidirectional context in understanding and representing words in a sentence. This contextual understanding enhances the accuracy of sentiment analysis by capturing the subtle nuances and dependencies between words.

In this article, our focus will be on harnessing the power of BERT for sentiment analysis in the Python programming language. We will explore the step-by-step process of utilizing pre-trained BERT models for sentiment analysis tasks, covering aspects such as text preprocessing, model loading, fine-tuning, and evaluation. By delving into practical examples and code snippets, we aim to provide a comprehensive understanding of how BERT can be employed to achieve state-of-the-art results in sentiment analysis.

Through this exploration, we will highlight the capabilities of BERT in effectively capturing the semantic meaning, context, and sentiment expressed in text data. By the end of this article, readers will have a solid foundation in applying BERT-based sentiment analysis techniques in Python and will be equipped with the knowledge to integrate sentiment analysis into their own projects or applications.

Photo by Steve Johnson on Unsplash


To follow this tutorial, you will need:

  • Basic knowledge of Python
  • Familiarity with NLP concepts
  • Understanding of deep learning models, especially neural networks and their architectures
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