Finding Influential Users in Social Networking using Sentiment Analysis

Shaha Al-Otaibi, Amal A. Al-Rasheed, Bashayer AlHazza, Hafsa Ahmad Khan, Ghadah AlShfloot, Maram AlFaris, Noura AlFari, Norah AlKhalaf, Nuha AlShuweishi


Text of the abstract:Social Networking platforms facilitate the sharing of information, ideas, and thoughts through constructing the virtual communities. Therefore, finding people in given social networking who are really have the power to influence others is more critical. For example, finding the right person who has an impact to support or contradict any opinion or bring more profits when he/she publishes an advertisement for a certain business or product. This problem can be modelled as finding influential users in social networking. There are different ways to find the influential users in these platforms and there are some criteria that should be considered. In this article, we propose a solution named Muatheer which help to determine the influential users in Instagram by scraping data using Instagram API and applying the sentiment analysis algorithm to classify the comments whether it is positive or negative, as well as using the unigram as a feature extraction method. Then, the sentiment analysis result combines with other factors to calculate the “Influence Ratio” that proposes to determine the actual influencer in a specific domain. The experiments were conducted using set of training datasets and the proposed algorithm gives a high accurate result using some metrices.

Full Text:



"Collins Dictionary," Collins , [Online]. Available:

P. J. Carrington, Models and Methods in Social Network Analysis, United States of America: Cambridge University, 2005.

M. Jäderlund, "Improving customer experience with wedding service providers through investigation of the ranking mechanism and sentiment analysis of user feedback on Instagram," diva-portal, 2019.

S. O. Edosomwan, "The history of social media and its impact on business," The Journal of Applied Management & Entrepreneurship , vol. 16, no. 3, pp. 79-91, 2011.

C. Holsapple, S.-H. H. Pakath and R. Pakath, "Business Social Media Analytics: Definition, Benefits and Challenges," in Conference: Americas Conference on Information Systems (AMCIS) , Savannah, GA, USA, 2014.

W. M.K., "Theoretical Framework," Colorado State, 2006 .

S. Al-Otaibi, A. Alnassar, A. Alshahrani, A. Al-Mubarak, S. Albugami, N. Almutiri and A. Albugami, "Customer Satisfaction Measurement using Sentiment Analysis," International Journal of Advanced Computer Science and Applications, vol. 9, no. No. 2, pp. 106-117, 2018.

B. B. •. P. C. Treleaven, "Social media analytics: a survey of techniques, tools and platforms," AI & SOCIETY, vol. 35, no. 1, pp. 90-03, 2014.

Z. O. Ajla Kirlić, "Measuring human and Vader performance on sentiment analysis," Invention Journal of Research Technology in Engineering & Management (IJRTEM), vol. 1, no. 12, pp. 43-44 , 2017.

R. Stair‏ and G. Reynolds, Principles of Information Systems, USA, 2014.

M. Tremelling, "5 Things You Didn’t Know You Could Do with Pixlee," [Online]. Available: [Accessed 5 10 2019].

socialbakers, "socialbakers," socialbakers, [Online]. Available: [Accessed 5 10 2019].

Q. Ma, X. Luo and H. Zhuge, "Finding influential users of web event in socialmedia," Concurrency Computat Pract Exper, vol. 31, 2019.

F. Erlandsson, P. Bródka, A. Borg and H. Johnson, "Finding Influential Users in Social Media Using Association Rule Learning," Entropy , vol. 18, 2016.

B. Shazad, K. H. Ullah, Zahoor-ur-Rehman, F. Muhammad, M. A. M. Irfan, R. Seungmin and Y. Nam, "Finding Temporal Influential Users in Social Media Using Association Rule Learning," Intelligent Automation And Soft Computing, vol. 26 , no. 1 , pp. 87-98, 2020.

Y.-M. Li, "Discovering influencers for marketing in the blogosphere," Information Sciences -Elsevier, vol. 181 , no. 23, pp. 5143-5157, 2011.

C. Hutto and E. Gilbert, "VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text," in Conference: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, USA, 2015.

B. Liu, "Sentiment Analysis and Opinion Mining," in Web Data Mining pp , Illinois - USA, University of Illinois at Chicago, 2011, pp. 459-526.

"Visual paradigm," [Online]. Available: [Accessed 20 11 2019].

I. API, "Instagram Developer," Instagram, [Online]. Available: [Accessed 9 Jan 2020].

riyam, "instagram-php-scraper," github, [Online]. Available: [Accessed 31 feb 2020].

abusby, "github," [Online]. Available: [Accessed 25 Feb 2020].

Instagram, "Libraries," [Online]. Available: [Accessed 3 March 2020].

repat, "instagram-php-scraperTag," [Online]. Available:

Instagram, "Endpoints," [Online]. Available: [Accessed 14 March 2020].

Instagram, "Comment," [Online]. Available: [Accessed 14 March 2020].

abubsy, "php-vadersentiment," [Online]. Available: [Accessed 20 March 2020].

cjhutto, "VaderSentiment," [Online]. Available: [Accessed 14 March 2020].

C. H. E. Gilbert, "VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text," Association for the Advancement of Artificial Intelli- gence, Atlanta, 2014.

abubsy, "vadersentiment," Feb 2020. [Online]. Available:


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.