In the first part of this series, we extracted adoptable cat and dog information from Petfinder. We found the tones used in the descriptions of the adoptable animals using IBM Watson's Tone Analyzer. These datasets were then combined and cleaned to create a single, unified dataset that can be analyzed with standard Python data analysis packages. In this post, we will explore the dataset and try to answer our original question. Is there a significant difference in tones used in adoptable animal descriptions depending on the species or other factors?
Many animals listed on Petfinder are also given a description by the shelter that provides further details and information on the pet. These descriptions are useful for increasing interest among potential adopters by helping to establish a more personal connection to the animal beyond just cute pictures (not to say I can't get enough of cute cat pictures). Do these descriptions vary in tone depending on the type of animal or the animal's age or other statistics? Through the combination of several Python libraries petpy, textacy, pandas, and the IBM Watson Tone Analyzer API, we will take the first step in answer these questions and more by cleaning and transforming the extracted data and adoptable pet descriptions from the Petfinder API.
- Data Science
- Linear Algebra
- Machine Learning
Page 1 / 1