  The scope of a lucrative career promoted by Google through its video
distribution platform YouTube has attracted a large number of users to become
content creators. An important aspect of this line of work is the feedback
received in the form of comments which show how well the content is being
received by the audience. However, volume of comments coupled with spam and
limited tools for comment classification makes it virtually impossible for a
creator to go through each and every comment and gather constructive feedback.
Automatic classification of comments is a challenge even for established
classification models, since comments are often of variable lengths riddled
with slang, symbols and abbreviations. This is a greater challenge where
comments are multilingual as the messages are often rife with the respective
vernacular. In this work, we have evaluated top-performing classification
models for classifying comments which are a mix of different combinations of
English and Malayalam (only English, only Malayalam and Mix of English and
Malayalam). The statistical analysis of results indicates that Multinomial
Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random
Forest and Decision Trees offer similar level of accuracy in comment
classification. Further, we have also evaluated 3 multilingual transformer
based language models (BERT, DISTILBERT and XLM) and compared their performance
to the traditional machine learning classification techniques. XLM was the
top-performing BERT model with an accuracy of 67.31. Random Forest with Term
Frequency Vectorizer was the best performing model out of all the traditional
classification models with an accuracy of 63.59.
