Open Access   Article

Predicting and Detecting Hectoring on Social Media Using Machine Learning

Sakshi Gujral1

1 Dept. of CSE, Indira Gandhi Delhi Technical University for Women, Delhi, India.

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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-8 , Page no. 173-176, Aug-2017


Online published on Aug 30, 2017

Copyright Sakshi Gujral . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sakshi Gujral , Predicting and Detecting Hectoring on Social Media Using Machine Learning, International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.173-176, 2017.

MLA Style Citation: Sakshi Gujral "Predicting and Detecting Hectoring on Social Media Using Machine Learning." International Journal of Computer Sciences and Engineering 5.8 (2017): 173-176.

APA Style Citation: Sakshi Gujral , (2017). Predicting and Detecting Hectoring on Social Media Using Machine Learning. International Journal of Computer Sciences and Engineering, 5(8), 173-176.

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The increase of use of Social networking sites in recent years has both pros and consequences. The idea is to have safer use of these Social media sites so as to obtain maximum benefits from them rather than having malicious effects from them. One of the misuses of these social networking sites like Twitter, Facebook, and Instagram is posting of absurd contents over these Social Medias. This content can be extremely harmful as it causes insult, depression, anxiety, peer pressure. This needs to be detected and reported for better use of social media. This measure will make an approach for better use of Internet yard. Hence, this research work aims at Predicting and detecting these harassing comments with the help of Machine learning Algorithms. The idea is to do Sentimental Analysis of the tweets obtained from Twitter, Pre-process them, apply Machine Learning algorithms along with Bag-Of-Words on the tweets and classify the tweets as positive and negative. Tweets classified as negative will help to detect the tweet as bullying or not. The proposed research work uses bag of words approach along with Laplace version of the Naive-Bayes classifier with Laplace function for Detection. For Prediction CART model with Bag-of-Words approach is used. The platform used here is R studio with various packages.

Key-Words / Index Term

Prediction, Detection, Hectoring, Bag-of-word ,Laplace, Confusion Matrix


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