Sentiment Analysis and Intent Recognition Using BERT

This repository contains code for performing sentiment analysis and intent recognition using the BERT (Bidirectional Encoder Representations from Transformers) model. The code is implemented in Python and leverages the Hugging Face Transformers library for BERT and TensorFlow for building and training the model.

Overview

This project consists of two main components:

1. Sentiment Analysis: The first part of the code trains a BERT-based model to perform sentiment analysis on text data. It's trained on a dataset with text samples and corresponding sentiment labels.

2. Intent Recognition: The second part of the code demonstrates how the same BERT model can be used for intent recognition. Given a user query, the model predicts the intent of the query based on a predefined set of intents.

Prerequisites

To run this code, you need to have the following libraries and tools installed:

• Python
• Transformers (Hugging Face library)
• TensorFlow
• NumPy

You can install the required libraries using pip:

pip install transformers tensorflow numpy

Usage

Training Sentiment Analysis Model

1. First, download the training dataset. You can specify the path to your dataset by changing the root_path variable in the script.

2. Run the code for training the sentiment analysis model. This script preprocesses the data, tokenizes it using BERT's tokenizer, and trains a model for sentiment analysis. The trained model is saved as 'sentiment_model'.

3. You can evaluate and fine-tune the model as needed. The code provides options for adjusting the model architecture and training parameters.

Intent Recognition

1. The code also provides a function for tokenizing input text for prediction.

2. The main() function demonstrates how to use the trained BERT model for intent recognition. It predicts the intent of a user query and prints the result. You can modify the user queries in the loop to test different inputs.

Customization and Enhancements

• Dataset: You can replace the training dataset with your own dataset for sentiment analysis. Ensure that the dataset has text samples and corresponding sentiment labels.

• Intent Set: To use the model for intent recognition, define your set of intents and modify the output_to_intent function to map the model's output to your intents.

• Fine-Tuning: You can fine-tune the BERT model for better performance on specific tasks by adjusting the model architecture, training parameters, and loss functions.

• Deployment: If you want to deploy the model as a chatbot or for automated intent recognition, you can integrate it into your application or web service.

Disclaimer

This code is intended as a starting point for building sentiment analysis and intent recognition models using BERT. Further testing, evaluation, and customization are needed for production use. Be aware of privacy and data handling considerations when deploying models.

Please use the code responsibly and ensure that you have the necessary rights to use any training data.

Feel free to contribute to this project and enhance its functionality!

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