Integrate ChatGPT (OpenAI API) into your research project (Part 3): Sentiment analysis in practice

In the previous articles of this series, we explored how to integrate ChatGPT into your research project using Qualtrics surveys and Excel. Now, let's delve into the practical application of sentiment analysis using the API.

Read more in this series:

Integrate ChatGPT (OpenAI API) into your research project (Part 1): Qualtrics survey

Integrate ChatGPT (OpenAI API) into your research project (Part 2): Excel

Integrate ChatGPT (OpenAI API) into your research project (Part 4): Conversation within surveys

Integrate ChatGPT (OpenAI API) into your research project (Part 5): Voice interaction within survey

Why Sentiment Analysis Matters

Sentiment analysis is the process of identifying and categorising emotions expressed in text. This can be incredibly valuable for researchers aiming to understand opinions, attitudes, and emotional responses related to their study.

Challenges and Solutions

Harnessing the power of ChatGPT for sentiment analysis presents exciting opportunities, but also unique challenges. Let's explore these challenges and discover solutions to unlock reliable and insightful results:

Challenge 1: Extracting Multiple Values

ChatGPT typically provides a single text response. To capture sentiment, confidence, and potentially emotion, we need a structured approach.

Solution: Craft Effective Prompts

• Role-playing: By assigning the role of a "sentiment classification professional," we guide the model towards provide more effective responses.
• Output formatting: Requesting the output in JSON format makes it easier to parse and extract the values for further analysis and integration into your research data.
• One-shot prompting: Providing a clear example of the expected JSON format helps the model understand the structure and content of the desired response.

Challenge 2: Ensuring Reliable Results

The inherent randomness of large language models can lead to variations in responses.

Solution: Leverage API Parameters

• temperature: This parameter controls the randomness of the output. Lower values (closer to 0) lead to more deterministic and consistent responses. Experiment with different temperature values to find the optimal balance between creativity and consistency for your project.
• max_tokens: This parameter limits the length of the response. Setting a reasonable limit ensures that the model focuses on the relevant aspects of the input and avoids unnecessary variations due to excessive output generation.

Challenge 3: Integrating Results into Data

Efficiently incorporating sentiment analysis results into research data is crucial.

Solution: Explore No-Code Tools

While programming languages offer powerful parsing capabilities, several no-code tools can help you extract and manage data from JSON responses:

• Online JSON Viewers: These tools allow you to visualise and navigate JSON data, making it easier to identify and extract the sentiment, confidence, and emotion values.
• Spreadsheet Software: Import the JSON response into a spreadsheet application like Excel or Google Sheets. Utilise built-in functions and formulas to extract and analyse the desired values.
• Data Visualisation Platforms: Consider platforms like Tableau or Power BI to visualise and explore the extracted sentiment data, uncovering trends and patterns.

Additional Considerations:

• Error Handling: Implement error handling mechanisms to deal with potential issues like API connection errors or unexpected response formats.
• Bias and Fairness: Be aware of potential biases in the training data of large language models and take steps to mitigate their impact on your analysis. Consider using diverse datasets and evaluating the model's performance across different demographics or subgroups.
• Ethical Considerations: When dealing with sensitive data or topics, ensure you comply with ethical guidelines and data privacy regulations. Anonymise data when necessary and obtain informed consent from participants.

Qualtrics example

Web service

Building on the previous guide's example, we've added 'temperature' and 'max-tokens' parameters to the request body, embedded 'APIError = error.message' data for error handling, and upgraded the model to gpt-4-turbo for improved performance.

Error handling

If 'APIError' is captured, the request will fallback to the gpt-3.5-turbo model.

JSON parsing

While there are numerous ways to parse JSON data, using an API can streamline the process of converting JSON strings into objects, especially when working within platforms like Qualtrics.

To facilitate this, I've developed a free API specifically for research projects: https://jsonparser-zirzyo5kda-uc.a.run.app. Feel free to integrate it into your workflow to efficiently handle JSON data conversion!

By integrating live sentiment analysis into your research projects using ChatGPT and the methods discussed, you can unlock new ways to interactive with your paticipants.

Remember to approach these techniques with a critical eye, considering potential biases and ethical implications.

With careful planning and implementation, sentiment analysis can become a valuable tool in your research arsenal, providing deeper insights and enriching your understanding of the human experience.