<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/b00b2422a1d94602acd67d4fda8e6115&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/b00b2422a1d94602acd67d4fda8e6115-00001.gif</thumbnail_url><duration>1603.543999999994</duration><title>Classification of Recipe Review Texts According to Problems Identified by Review</title><description>In this video, I present my capstone project for Data 698 at CUNY SPS. I discuss the classification of recipe review texts based on the problems identified by reviewers. I explain the methodology used, including unsupervised and supervised learning techniques. I also highlight the challenges faced and the results achieved. Watch this video to learn more about how we analyzed recipe reviews and predicted the problems they identified.</description></oembed>