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

TL;DR

This paper evaluates traditional and transformer-based models for classifying multilingual comments on YouTube, highlighting XLM's superior performance in handling mixed-language comments.

Contribution

It compares traditional machine learning models with multilingual transformers for comment classification in a multilingual context, which is less explored.

Findings

XLM achieved 67.31% accuracy, outperforming other models.

Traditional models like Random Forest achieved up to 63.59% accuracy.

Multilingual transformers are effective for mixed-language comment classification.

Abstract

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.