This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Building an Emotional State Detection AI Agent: A Simplified Approach
Emotional state detection is a crucial aspect of various applications, including mental health monitoring, customer service chatbots, and human-computer interaction. In this post, we'll delve into a simplified approach to building an emotional state detection AI agent using a combination of decision trees and natural language processing (NLP) in Python.
The Problem and Approach
Our goal is to develop an AI agent that can accurately detect emotional states from text inputs. We'll use a decision tree classifier to classify the text into predefined emotional categories, such as happy, sad, angry, or neutral. This approach is beneficial for its simplicity and interpretability, making it an excellent starting point for beginners and experts alike.
The Code Snippet
python
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.tokenize imp...
---
*This post was originally shared as an AI/ML insight. Follow me for more expert content on artificial intelligence and machine learning.*
This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez | Sciencx (2025-09-24T19:59:38+00:00) **Building an Emotional State Detection AI Agent: A Simplifi. Retrieved from https://www.scien.cx/2025/09/24/building-an-emotional-state-detection-ai-agent-a-simplifi/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.