Sentiment Analysis Using Fine-Tuned BERT on GoEmotions Dataset

Executive Summary

A leading client in the social media analytics space sought a solution to classify user-generated comments into emotional categories to better understand their audience and enhance engagement. By fine-tuning a pre-trained BERT model on the GoEmotions dataset, we delivered a high-accuracy sentiment analysis model. This Proof of Concept (PoC) demonstrated the feasibility of implementing AI-driven sentiment analysis for their needs.

Project Overview

The goal was to develop a sentiment analysis model capable of identifying 27 emotion categories (e.g., admiration, amusement, anger) and a neutral class from textual comments. The PoC involved fine-tuning a BERT model using the GoEmotions dataset and testing its applicability on the client’s dataset of user comments.

Plan of Action

• Understand the client’s objectives and dataset requirements.
• Leverage the GoEmotions dataset to fine-tune a pre-trained BERT model.
• Validate and test the model’s performance on unseen client data.
• Optimize the model for deployment and scalability.

Conclusion

This PoC demonstrated the effectiveness of fine-tuning a BERT model for multi-class sentiment analysis using the GoEmotions dataset. The results provided actionable insights for the client and paved the way for scaling the solution to handle larger datasets. The project successfully validated the feasibility of deploying an AI-driven sentiment analysis system, positioning the client for improved emotional understanding and audience engagement.