Generating Synthetic Data and Configuring Python Environment for Sentiment Analysis

Artificial Intelligence, Machine Learning, Agentic AI, and so many concepts are of constant discussion in the technology landscape today. So much so that, many individuals are overwhelmed by the varying concepts, or whether or not specific concepts apply or are easily translatable into their domains. With many of the secret societies and gate keepers of the AI space there is a large group of individuals with constant questions related to how they can get started bringing their idea to life. What skills do I need? What tools are available to me? I don't have the budget for a specific cloud provider, but I really want to build today.

In a previous post, "Navigating Artificial Intelligence (AI): A Guide to Navigating the Digital Transformation Age, we mentioned a few options that you can leverage to begin your journey. This is in addition to the continuously growing Huggingface community, which has various models, datasets and frameworks available to bring your idea to life. It's also a great option to develop your own models or datasets to contribute to the broader community, helping individuals reduce technical debt while bringing their idea to life. You never know what could come of your contributions.

In this article, we will use synthetic data generate via a simple C# script, to utilize pre-built and build sentiment analysis models. In a previous article, "Getting Started with ML.NET for Machine Learning: Building LLMs for AI Applications, we used ML.NET to analyze datasets. In this article we will do the same, generating our own data to better outline model analysis.

Before we begin, sentiment analysis is a natural language processing technique that is used in machine learning to determine the "sentiment" of provided text. In this instance, if we have a completed survey by "x" number of customers, how do we then apply sentiment analysis to the responses to determine sentiment in a way that provides us with actionable insights. In this example, we hope to equip readers with tools to generate synthetic data and configure a Python environment for building their own sentiment analysis model.

Understanding Synthetic Data

Synthetic data is artificial data that is generated by a specific method, to use to train learning models or plug into some other application pipeline. When using synthetic data that closely represents the data that is used in a production system, we are able to eliminate privacy, bias, and data scarcity concerns that would otherwise get introduced when testing and validating models in a production system. In this instance, we have 500 survey responses, submitted by a diverse group of patients or customers.

Each response to the survey incorporates an assessmentID as the identifier. For each response we also collect the overall sentiment, ethnicity, demographic, education level, and an assigned keyword to decipher between the different types of health assement. Within our data, we notice that the demographic information lacks consistency and unification regarding the category it is to support. Meaning, age category vs. disability vs. economic class and citizenship is grouped into one column. At this stage, it is not clear how to handle this but important to note upon initial assessement of data attributes.

CSV file containing the initial data items and their respective data attributes within our dataset.

It's important to note that data in this dataset is generic data generated via a script written in C# and can be used to generate new data or modified to change data attributes or add and remove specific attributes.

Configuring Python Environment with Pipenv

Prior to beginning, its important to understand your own set up vs. the setup of the steps that you may follow. In this instance, we need to ensure we have python installed, while Pipenv is used to create virtual environments where you are able to isolate dependencies to their respective project. For instance, one project may call for an older version of python that includes some functionality (fx), but another project may need to use the latest version of python which does not include (fx) functionality. Virtual environments will act as an isolated space where the necessary dependencies needed are installed in a way that does not cause issues with a different set of dependencies in another space or project and its python libraries.

Step-by-step instructions for installing Pipenv:

The list of steps within this section, assume that you already have python installed in your local environment. You can verify installation and versioning using the following commands:

$python --version

Note: If either command returns as not being found, verify your $PATH information to ensure it is installed and accessible from the respective directory. If not, install before proceeding. If already installed, proceed to the next steps.

Install pipenv, the dependency manager for python environments

$pip install --user pipenv

Note: The "--user" flag is a user scheme for installing. It allows installation within an alternative location that is associated with a user, opposed to the default install location. See this page for more information on pipenv installation settings.

Initialize a new project

$pipenv install

This step creates a pipfile within the directory. It is used to maintain track of any dependencies the project needs, for functionality and in the instance you need to reinstall them. You'll need to pass the name of a specific package or "--dev" flag to install all dev packages.

Building a Sentiment Analysis Model

• Overview of sentiment analysis and its relevance.

• Steps to import and use the synthetic dataset in Python:

- Loading the dataset with Pandas.

- Preprocessing the data for sentiment analysis.

• Overview of machine learning frameworks and libraries suitable for sentiment analysis:

- Discussion of popular Python libraries like Scikit-learn and TensorFlow.

• Step-by-step guide to creating, training, and evaluating a sentiment analysis model:

- Selecting the appropriate algorithms.

- Hyperparameter tuning for optimal performance.

- Evaluating the model with metrics like accuracy and F1 score.

Visualization and Results

• Importance of visualizing data and model results.

• Tools and libraries for visualization.

• Demonstrating results through charts and graphs.

  
  

Conclusion

• Synthetic data generation to model implementation process recap.

• Encouragement to explore further and modify the model for personal projects.

• Final thoughts on the potential of synthetic data in machine learning and sentiment analysis.

  
  

Call to Action

• Share your own experiences with synthetic data and sentiment analysis.

• Comment with questions or share their projects related to this topic.