  As a YouTube channel grows, each video can potentially collect enormous
amounts of comments that provide direct feedback from the viewers. These
comments are a major means of understanding viewer expectations and improving
channel engagement. However, the comments only represent a general collection
of user opinions about the channel and the content. Many comments are poorly
constructed, trivial, and have improper spellings and grammatical errors. As a
result, it is a tedious job to identify the comments that best interest the
content creators. In this paper, we extract and classify the raw comments into
different categories based on both sentiment and sentence types that will help
YouTubers find relevant comments for growing their viewership. Existing studies
have focused either on sentiment analysis (positive and negative) or
classification of sub-types within the same sentence types (e.g., types of
questions) on a text corpus. These have limited application on non-traditional
text corpus like YouTube comments. We address this challenge of text extraction
and classification from YouTube comments using well-known statistical measures
and machine learning models. We evaluate each combination of statistical
measure and the machine learning model using cross validation and $F_1$ scores.
The results show that our approach that incorporates conventional methods
performs well on the classification task, validating its potential in assisting
content creators increase viewer engagement on their channel.
