Total views : 105

Finding influential opinion by filtering commercial tweets


  • Department of Computer Science, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 139-701, Korea;


Objectives: This paper aims to gather and categorize valuable tweets that are shared by many people regarding the opinions expressed in Social Network Services (SNS)in real time. Methods/Statistical Analysis: Among many SNS, we have targeted Twitter which has excellent data accessibility. To find the comments on the current hot issue keywords, Google and Twitter Trends Keywords were utilized in the search. At first, the most retweeted tweets were gathered, but contrary to our expectations, most of them were general news that did not require analysis or marketing-related advertisements, etc., so they were classified. We solved this issue by making use of machine learning. Findings: Since media and celebrities have many followers, more of their tweets are retweeted compared to the average number of retweets per account. Therefore, because opinions should not be distinguished as influential just by their retweet numbers, the model made from the training data gathered in this study classified the tweets to analyzing the opinion mining. The evaluation of classification results showed 84.8% accuracy, and the evaluation of new tweets showed an accuracy of 84%. It seems that much more accurate results could be predicted with more training data.Improvements/Applications: A program allowingusers to find influential opinions about real-time trending search terms has been developed.


Influential opinion, Machine Learning, SNS, Spam Detection, Twitter.

Full Text:

 |  (PDF views: 102)


  • Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford; 2009. p. 1–6.
  • Nivedha R, Sairam N. A machine learning based classification for social media messages. Indian Journal of Science and Technology. 2015; 8(16):1–4.
  • Lee R, Wakamiya S, Sumiya K. Discovery of unusual regional social activities using geo-tagged microblogs.World Wide Web. 2011; 14(4):321–49.
  • Wang AH. Don’t follow me: Spam detection in twitter.Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT); 2010. p. 1–10.
  • Mccord M, Chuah M. Spam detection on twitter using traditional classifiers. International Conference on Autonomic and Trusted Computing; 2011. p. 175–86
  • Nalini K, Sheela LJ. Classification using Latent Dirichlet allocation with Naive Bayes classifier to detect cyber bullying in twitter. Indian Journal of Science and Technology.2016; 9(28):1–5.
  • Vinithra SN, Selvan SJA, Kumar MA, Soman KP. Simulated and self-sustained classification of twitter data based on its sentiment. Indian Journal of Science and Technology. 2015; 8(24):1–7.
  • Junaid MB, Farooq M. Using evolutionary learning classifiers to do MobileSpam (SMS) filtering. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation; 2011. p. 1795–802.
  • Kawade DR, Oza KS. SMS spam classification using WEKA.International Journal of Electronics Communication and Computer Technology. 2015; 5:43–7.
  • Bouckaert RR. Bayesian network classifiers in Weka.Department of Computer Science, University of Waikato; 2004.
  • Drazin S, Montag M. Decision tree analysis using WEKA.Machine Learning-Project II, University of Miami; 2012. p.1–3.
  • Liaw A, Wiener M. Classification and regression by random Forest. R news. 2002; 2(3):18–22.


  • There are currently no refbacks.

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