To Discover Trolling Patterns in Social Media: Troll Filter

Authors(3) :-Pooja M. Tayade, Shafi S. Shaikh, Dr. S. N. Deshmukh

Troll - the word itself defines everything about bullying or harassing someone. Trolling is an important problem in the online world. Emotions or feelings perform a vital role in successful and effective communication between humans. However, this human communication is getting worst if there is a group of people who enjoy targeting someone and trolling, this happens in different social media like Facebook and Twitter. In this paper tried to filter out comments, which are negative or insulting. Goal of this paper is to identify the targets of trolls, so as to prevent trolling before it happens. For this purpose used sentiment analysis (Positive or Negative) through machine learning. The major focus of this paper was on comparing different machine learning algorithms for the task of sentiment classification. For classification, many classifiers are available but results are very promising with Naive Bayes, Support Vector Machines (SVM) and Maximum Entropy (MaxEnt) classifiers. The major findings were evaluated that the Support Vector classifier provides the highest classification accuracy for this domain.

Authors and Affiliations

Pooja M. Tayade
Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
Shafi S. Shaikh
Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
Dr. S. N. Deshmukh
Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

Sentiment Analysis, Unigram – Bigram dependencies, Training data, Test data, Tweets, classifiers-SVM, Naïve-Bayes, MaxEntropy

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 698-703
Manuscript Number : CSEIT1725158
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Pooja M. Tayade, Shafi S. Shaikh, Dr. S. N. Deshmukh, "To Discover Trolling Patterns in Social Media: Troll Filter ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.698-703, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725158

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