Degree

Master of Science in Computer Science

Department

Department of Computer Science

Faculty / School

Faculty of Computer Sciences (FCS)

Date of Submission

2020-12-30

Supervisor

Dr. Sajjad Haider, Professor, Department of Computer Science

Document type

MSCS Survey Report

Abstract

The Internet is a place that ties individuals, eliminates distances and brings everyone closer. Nowadays, people help and greet each other, share ideas, and recommend things on social media. Platforms like Facebook, Twitter and Instagram are becoming a great source of exchanging information.

As per recent statistics, Facebook has around 1 billion users and Twitter has more than 300 million users. People who use these platforms and interact with others generate massive data as they exchange their ideas by comments, chats and likes/dislikes on posts, pictures, new places or campaigns launched. This kind of data contains both negative and positive aspects. It helps us in understanding the userbase and in identifying what is important and what is not. For instance, reviews about movies and places can help us in picking the right spot and thing.

In Pakistan, we have a huge market for those who uses social media platforms for entertainment purposes or sharing ideas. Most people use English/Urdu/Roman-Urdu language for exchanging information and ideas on such platforms. This research survey aims to analyze approaches from previous published research to experiment and to identify the techniques and algorithms that work best for social media comments posted in English/Urdu/Roman-Urdu languages.

In this work, data consisting of Urdu and Roman-Urdu languages was gathered to create a machine learning model to classify sentiments. Results show that SVM and Naïve Bayes algorithms performed well and gave an accuracy of 79% for Roman-Urdu and 72% accuracy for Urdu sentences.

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https://ils.iba.edu.pk/cgi-bin/koha/opac-detail.pl?biblionumber=114094

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