Generative AI Certificate Q&A: What type of machine learning to search social media for topics and classify the comments

Question

You work for a political organization that does sentiment analysis of social media networks. Politicians look to your service to see how people feel about certain difficult topics. Your organization has developed an artificial neural network that can search social media for topics and classify the comments as strongly agree, neutral, and strongly disagree.

What type of machine learning are you using?

A. unsupervised learning binary classification
B. variational auto encoding generative AI
C. reinforcement learning unsupervised clustering
D. supervised learning multiclass classification

Answer

D. supervised learning multiclass classification

Explanation

The answer to your question is D. supervised learning multiclass classification. Let me explain why.

Machine learning is a branch of artificial intelligence that uses data and algorithms to enable machines to learn from data and make predictions or classifications. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

• Supervised learning is a type of machine learning where the machine learns from labeled data, meaning the data has a known output or target variable. The machine uses the data and an algorithm to learn a function that maps the input to the output. The goal of supervised learning is to make accurate predictions or classifications for new data that has not been seen before. There are two main types of supervised learning: regression and classification.
  • Regression is a type of supervised learning where the output or target variable is continuous, meaning it can take any value within a range. For example, predicting the price of a house based on its features is a regression problem.
  • Classification is a type of supervised learning where the output or target variable is discrete, meaning it can take only a finite number of values or categories. For example, predicting whether an email is spam or not is a classification problem.
• Unsupervised learning is a type of machine learning where the machine learns from unlabeled data, meaning the data has no known output or target variable. The machine uses the data and an algorithm to discover patterns, structures, or hidden features in the data. The goal of unsupervised learning is to find useful information or insights from the data without any guidance or supervision. There are two main types of unsupervised learning: clustering and dimensionality reduction.
  • Clustering is a type of unsupervised learning where the machine groups similar data points together based on some measure of similarity or distance. For example, finding customer segments based on their purchase behavior is a clustering problem.
  • Dimensionality reduction is a type of unsupervised learning where the machine reduces the number of features or variables in the data while preserving as much information as possible. For example, reducing the size of an image while keeping its quality is a dimensionality reduction problem.
• Reinforcement learning is a type of machine learning where the machine learns from its own actions and experiences in an environment. The machine does not have any labeled data or explicit feedback, but instead learns by trial and error. The machine receives rewards or penalties for its actions and tries to maximize its total reward over time. The goal of reinforcement learning is to find the optimal policy or strategy for the machine to act in a given situation. For example, teaching a robot to walk or play chess is a reinforcement learning problem.

Now, let’s look at your scenario. You have developed an artificial neural network that can search social media for topics and classify the comments as strongly agree, neutral, and strongly disagree. This is clearly a classification problem, since you are trying to assign each comment to one of three categories.

Moreover, this is a supervised learning problem, since you need labeled data to train your neural network, meaning you need some comments that have been already classified by human experts or annotators.

However, this is not a binary classification problem, since you have more than two categories to choose from. A binary classification problem would be if you only had two categories, such as spam or not spam. Instead, this is a multiclass classification problem, since you have multiple categories (three in this case) to choose from.

Therefore, the type of machine learning you are using in your scenario is supervised learning multiclass classification.

Reference

• 3 Types of Machine Learning You Should Know | Coursera
• Machine learning – Wikipedia
• Types of Machine Learning – Javatpoint
• What Is Sentiment Analysis? What Are the Different Types? | Built In
• Machine Learning Approach for Sentiment Analysis (opengenus.org)
• Sentiment Analysis of Social Media Networks Using Machine Learning | IEEE Conference Publication | IEEE Xplore
• Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model | SpringerLink

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