IIUM Repository

Social network analysis using Python data mining

Gunawan, Teddy Surya and Abdullah, Nur Aleah Jehan and Kartiwi, Mira and Ihsanto, Eko (2020) Social network analysis using Python data mining. In: 8th International Conference on Information Technology for Cyber and IT Service Management (CITSM 2020), 23-24 Oct 2020, Pangkalpinang. (In Press)

[img] PDF - Accepted Version
Restricted to Registered users only

Download (888kB) | Request a copy
[img] PDF (Presentation Certificate)
Restricted to Repository staff only

Download (218kB) | Request a copy

Abstract

Analyzing public information from social networking sites could produce exciting results and insights on the public opinion of almost any product, service, or behavior. One of the most effective and accurate public sentiment indicators is through social networks data mining, as many users tend to express their opinions online. The internet’s advanced technology has managed to increase activity in blogging, tagging, posting, and online social networking. As a result, people are starting to grow interested in mining these vast data resources to analyze opinions. Sentiment analysis is one of the computational techniques of opinion, sentiments, and the variety of texts subjectivity. In this paper, the methodology of determining these public opinions are discussed. The development of a program for sentiment analysis is done to create a platform for social network analysis. This paper also discusses the sentiment analysis design, gathering data, training the data, and visualizing the data using the Python library. Finally, a platform is designed in order for other users to search the sentiment results of particular topics of interest. A total of 3000 Reddit data and 3000 Twitter data has been gathered, cleaned, analyzed, and visualized in this research. The analysis has produced an excellent percentage result of 83% and 77% for Twitter and Reddit data, respectively. Moreover, the GUI platform has been built using the Tkinter library.

Item Type: Conference or Workshop Item (Invited Papers)
Additional Information: 5588/85062
Uncontrolled Keywords: Social network analysis, sentiment analysis, Twitter, Reddit, Python.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Kulliyyah of Information and Communication Technology > Department of Information System
Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User: Dr Teddy Surya Gunawan
Date Deposited: 23 Nov 2020 04:06
Last Modified: 23 Nov 2020 04:06
URI: http://irep.iium.edu.my/id/eprint/85062

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year