Sentiment Analysis for Divorced Parents' Communication Enhancement

This sentiment analysis project was developed for a nonprofit organization dedicated to improving parenting dynamics for children of divorced parents. The objective of the project was to analyze messages exchanged between parents through their accounts on a website, using a sentiment classification system. The messages were labeled into three categories: aggressive, neutral, and positive, aimed at assessing the tone and emotional content of the communication.

 

The approach utilized in this project involved the application of TextBlob, a Python library for processing textual data. Leveraging supervised modeling techniques, the project aimed to predict and categorize messages based on their sentiment polarities. By employing natural language processing and machine learning, the model was trained to identify aggressive, neutral, and positive language patterns within the messages.

 

The significance of this sentiment analysis model lies in its potential to provide valuable insights into the emotional state of divorced parents during their communication. This information can assist the nonprofit organization in offering tailored support and guidance to parents, ultimately contributing to a more conducive co-parenting environment for the well-being of the children involved.