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The scope of a lucrative career promoted by Google through its videodistribution platform YouTube has attracted a large number of users to becomecontent creators. An important aspect of this line of work is the feedbackreceived in the form of comments which show how well the content is beingreceived by the audience. However, volume of comments coupled with spam andlimited tools for comment classification makes it virtually impossible for acreator to go through each and every comment and gather constructive feedback.Automatic classification of comments is a challenge even for establishedclassification models, since comments are often of variable lengths riddledwith slang, symbols and abbreviations. This is a greater challenge wherecomments are multilingual as the messages are often rife with the respectivevernacular. In this work, we have evaluated top-performing classificationmodels for classifying comments which are a mix of different combinations ofEnglish and Malayalam (only English, only Malayalam and Mix of English andMalayalam). The statistical analysis of results indicates that MultinomialNaive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), RandomForest and Decision Trees offer similar level of accuracy in commentclassification. Further, we have also evaluated 3 multilingual transformerbased language models (BERT, DISTILBERT and XLM) and compared their performanceto the traditional machine learning classification techniques. XLM was thetop-performing BERT model with an accuracy of 67.31. Random Forest with TermFrequency Vectorizer was the best performing model out of all the traditionalclassification models with an accuracy of 63.59.

Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code)