Decoding Emotions: Sentiment Analysis with BERT

Author(s): Souradip Pal

Originally published on Towards AI.

Dive into the world of NLP and learn how to analyze emotions in text with a few lines of code!

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Imagine you’re at a bustling party, surrounded by a crowd with conversations buzzing all around. Some people are thrilled, some are frustrated, and others seem indifferent. Now, imagine being able to instantly understand the mood of every conversation happening in that room. That’s a bit like what BERT does — except instead of people, it reads text.

BERT, short for Bidirectional Encoder Representations from Transformers, is a powerful machine learning model developed by Google. It’s like a well-read friend who can understand the context and emotion behind each word in a sentence, giving us a deeper insight into what people are really saying. In this post, we’ll harness the power of BERT for sentiment analysis, learning to extract emotional insights from text in a way that’s both efficient and surprisingly straightforward.

Let’s jump in and set up our sentiment analysis tool using BERT!

The below Repo contains the code that I have discussed in this blog, feel free to check it out and play with it.

Contribute to Souradip121/Financial-Sentiment-Analysis-with-NLP development by creating an account on GitHub.

github.com

To get started, you’ll need a few libraries installed on your system. Don’t worry if this seems intimidating… Read the full blog for free on Medium.

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Published via Towards AI