With the rise of YouTube as the primary source of children's entertainment, concerns have been raised about the lack of quality content. The absence of any video quality indicator and no certification process, even for videos with billions of views, aggravates parental worries. To address these concerns, we propose a machine-learning based approach to assess the language quality of children's videos. We use labeled data from a movie rating website (meant for parents to decide on a movie's appropriateness) to train a deep-learning model for rating the language used in YouTube Kids' videos. We further augment the deep-learning model with a Large Language Model (LLM) that generates a text summary stating the reason for the rating and highlighting keywords and phrases inappropriate for children. Using the proposed system, we analyze over 85,000 videos from the top 100 YouTube Kid's channels and compare them against Disney/Pixar movies that are certified for children's viewing. Our analysis reveals that certified movies generally have a lower language rating than YouTube Kid's channels (lower is better), and animations on YouTube usually have lower language ratings than non-animations on YouTube. Our analysis highlights a need for more stringent guidelines for video creators creating children's content.