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.
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；Indian Institute of Information Technology and Management-Kerala（喀拉拉邦信息技术与管理学院）
Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil, Malayalam and English
DataWeave；Exotel
Theedhum Nandrum是一个使用两种方法的情感极性检测系统-基于随机梯度下降（SGD）的分类器和基于长短期记忆（LSTM）的分类器。我们的方法利用了语言功能，如使用表情符号，选择脚本和代码混合，这些功能在为Dravidian Codemix - FIRE 2020任务指定的数据集中非常明显。SGD的超参数使用GridSearchCV进行调整。我们的系统在泰米尔语英语中排名第四，加权平均F1得分为0.62，在马拉雅拉姆语英语中排名第九，得分为0.65。在任务截止日期后，我们使用基于Logistic回归的模型获得了泰米尔语英语的加权平均F1分数为0.77。这种性能大大优于该数据集上排名最高的分类器。我们使用特定语言的Soundex来协调代码混合数据中的拼写变体，这似乎是Soundex的一个新应用。我们的完整代码发布在github网站https://github.com/oligoglot/theedhum-nandrum上。
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text
National University of Ireland Galway（爱尔兰国立高威大学）；ULTRA Arts and Science College（ULTRA文理学院）；Eastern University, Sri Lanka（斯里兰卡东方大学）；Sri Sivasubramaniya Nadar College of Engineering（斯里西瓦苏布拉马尼亚·纳达尔工程学院）；Indian Institute of Information Technology and Management-Kerala（印度喀拉拉邦信息技术与管理学院）；Indian Institute of Information Technology Tiruchirappalli（印度蒂鲁吉拉伯利信息技术学院）；University of Moratuwa, Sri Lanka（斯里兰卡莫拉图瓦大学）
Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling
Indian Institute of Information Technology Tiruchirappalli（蒂鲁奇拉帕利印度信息技术学院）；ULTRA Arts and Science College（ULTRA文理学院）；Eastern University（东方大学）；Kongu Engineering College（孔古工程学院）；Sri Sivasubramaniya Nadar College of Engineering（斯里·西瓦苏布拉马尼亚·纳达尔工程学院）；Insight SFI Research Centre for Data Analytics, National University of Ireland Galway（爱尔兰国立大学戈尔韦分校SFI洞察数据分析研究中心）
KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text
Institute of Management and Research, Jalgaon, Affil- KBC North Maharashtra University（贾尔冈管理与研究学院，隶属于KBC北马哈拉施特拉大学）；School of Computer Sciences, KBC North Maharashtra University（KBC北马哈拉施特拉大学计算机科学学院）；Copenhagen Business School（哥本哈根商学院）；BMCC college of commerce, Pune（BMCC商学院，浦那）
DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text
National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；ULTRA Arts and Science College（ULTRA艺术与科学学院）；Cardiff University（卡迪夫大学）；Indian Institute of Information Technology and Management-Kerala（印度喀拉拉邦信息技术与管理学院）
Pegasus@Dravidian-CodeMix-HASOC2021: Analyzing Social Media Content for Detection of Offensive Text
Indian Institute of Information Technology Tiruchirappalli（蒂鲁吉拉帕利信息技术学院）；Kongu Engineering College（贡古工程学院）；Sri Krishna Adithya College of Arts and Science（斯里克里希纳阿迪蒂亚文理学院）；Sultan Idris Education University（苏丹伊德里斯教育大学）
为了解决检测攻击性评论/帖子的难题，这些评论/帖子是相当非正式的，非结构化的，错误的和代码混合的，我们在这篇研究论文中介绍了两种创造性的方法。社交媒体平台上的攻击性评论/帖子可能会影响个人，团体或未成年人。为了对两种流行的德拉威语言（泰米尔语和马拉雅拉姆语）的评论/帖子进行分类，作为HASOC - DravidianCodeMix FIRE 2021共享任务的一部分，我们采用了两个基于transformer的原型，它们成功地在所有任务中排名前8位。我们的方法的代码可以查看和使用。
