Sentiment Analysis Task

This project serves as an acceptance task for the SAIS Hackathon and is designed to test your skills in natural language processing (NLP) by building a sentiment analysis model using the OpenAI API. The goal is to accurately classify texts into three categories: positive, negative, or neutral.

Submission Guidelines ⚠️

• Fill the team/participant information in the Information header.
• Complete the implementation of the sentiment_analysis function in the sentiment_analysis.py file.
• Ensure your code is well-commented and follows the guidelines specified below.
• Commit your final changes to the main branch of your repository before the deadline.

Information

• Team/Participant name: <YOUR_TEAM/PARTICIPANT_NAME>
• Email: <YOUR_EMAIL>

Objective

Your task is to implement a Python program that reads a file with the list of text strings (separated by a newline character) and returns a list of sentiment labels corresponding to each text string. The sentiments are classified as either 'positive', 'negative', or 'neutral'.

To solve this task, you will need to integrate the OpenAI API and invoke one of their chat-based models to perform sentiment analysis on the input text strings.

Requirements

• Python 3.8 or later
• OpenAI API key

Setup

1. Clone the repository: Start by cloning this repository to your local machine or development environment.
2. Install dependencies: Ensure that Python is installed on your system. You will need the openai library, which can be installed using pip:

pip install openai

3. API Key: You need to have access to an OpenAI API key. Set your API key in your environment variables:

export OPENAI_API_KEY='your_api_key_here'

Implementation Guide

1. Modify the function: You will find a skeleton function in sentiment_analysis.py. Implement the logic which calls the OpenAI API to perform sentiment analysis.

2. API Integration:

• Use the OpenAI Documentation to invoke OpenAI's chat-based model to get the best-fitting sentiment for the provided input text.

3. Testing: Test your function with different types of texts to ensure it accurately identifies the sentiment.

4. Error Handling: Implement error handling to manage potential issues during API calls.

Example Usage

Below is an example of how you might run the program:

python main.py data/texts.txt

Evaluation Criteria

• Accuracy of sentiment classification
• Code quality and efficiency
• Proper use of the OpenAI API
• Handling of edge cases and potential API errors

Support

If you encounter any issues, please reach out on the class discussion forum.