Author

Ali Zia

Degree

Master of Science in Computer Science

Faculty / School

Faculty of Computer Sciences (FCS)

Department

Department of Computer Science

Date of Submission

2019-12-31

Advisor

Shams Naveed Zia

Project Type

MSCS Survey Report

Abstract

With the emergence and traction of the social media platforms world-wide, people now prefer having quick access to the maximum possible information about any global news. For this purpose, these platforms are the first go to place for any review, news or any updates. Any viral topic catches attention throughout the world and starts trending within minutes. This impact of social media globally has changed the overall thinking, attention to the news and details of the people.

Recently, there have been various news and happenings pertaining to politics, technology that gained a lot of attention throughout the world. People tend to post, tweet, review and blog about these events on the various social media platforms that are available. There are sentiments and feelings behind all of this data that is data that is generated and written on these platforms. People from around the world express their happiness, anger, sadness and all other emotions through these sites while others are interested in knowing these opinions. In order to know the overall sentiment and views of the users is a difficult task since a lot of data is available in variable form.

For this purpose, various techniques are available for data processing and analyzing the sentiments from this data. The major techniques being used are Deep Learning and Machine Learning. Both of them are further divided into individual tasks that these techniques perform. The paper performs a review and analysis of these techniques together with the researches pertaining to them. We also look into the accuracy that has been achieved as a result of these techniques.

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