This repository is based on my research paper titled "A Machine Learning Approach to Classify Anti-social Bengali Comments on Social Media". The paper is accepted by IEEE. You can found this paper in the following link: https://ieeexplore.ieee.org/document/9836407.

Here, you can found the basic coding part of the research. We have collected 7000+ bengali comments from social media. Please see the dataset folder.

• Comments: Collect preprocessed comments from social media
• Annotation: Annotated the comments as social or anti-social
• Building Model Using the data, build a ML model
• Classification Model can classify the new comments

• “আর ভারতে যাইয়া পুজোর উদ্বোধন করে আসো"
  (Travel to India to inaugurate Puja.)
  -Although no obscene words are used in this sentence, it offends a community's religious emotions. So, it is an anti-social comment.

• “হিরো কনডম সবসময় এক নাম্বার"
  (Hero condom is always number one.)
  -Here no slang word is used or no one is dehumanized. So, it is not an anti-social comment.

• "মা হিসেবে আপনি জাতির গর্ব কারণ আপনার সন্তানকে আপনি অনেকগুলা বাবা উপহার দিয়েছেন"
  (As a mother you are the pride of the nation because you have given many fathers to your child.)
  -In this scenario, an individual character such as a mother was questioned, which is not socially acceptable. Hence this is termed as anti-social.

• “এটা ই যেন শেষ আন্দোলন হয়"
  (Hope it will be the last movement.)
  -It yearned for the end of a movement indicating peace. which is socially acceptable.

• Comments on social media were analysed based on socially acceptable or not rather than sentiment analysis.
• Made a dataset containing 2000 Bengali comments collected from popular social media.
• Applied 5 traditional and modern machine learning algorithms and compared the performance of the models based on these algorithms
• Built N-Gram (uni-gram, bi-gram, and tri-gram) language models.